System and method for generating commodity flow information

ABSTRACT

Disclosed is method including receiving digital vessel data for a global fleet of vessels, the digital vessel data being one or more of AIS data, image data or radar data and combining one or more of pieces of data. The method includes inferring, based on the first combined data, a loaded/empty status of a vessel or a cargo. The method includes combining other data to yield second combined data, receiving data regarding one or more of supply, demand, and amount of available cargo to yield third combined data, generating information relating to a supply of vessels available to load at a specified port and/or deliver a cargo to a specified port, in each case within a specified period of time and generating suggestions for one or more vessels regarding future routes based on the data.

PRIORITY CLAIM

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/200,774, filed Jul. 1, 2016, which is a continuation of U.S.patent application Ser. No. 12/997,295, filed May 27, 2011, now U.S.Pat. No. 9,384,456, issued Jul. 5, 2016, which is a national phase ofPCT/US2009/048545, filed Jun. 25, 2009, which claims priority to U.S.Provisional Application Nos. 61/076,317, filed Jun. 27, 2008,61/120,136, filed Dec. 5, 2008, 61/159,854, filed Mar. 13, 2009, and61/162,008, filed Mar. 20, 2009, the contents of which are incorporatedherein by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure provides concepts that are in the field ofcommunication and database systems and more particularly in the field ofacquisition, analysis, inference and presentation of global commodityflow data.

BACKGROUND

At present, only certain amounts of discrete information regarding theglobal flow of various commodities is available in real-time or nearreal-time. Real-time or near real-time information is of particularinterest to commercial traders, economists, and others. Maritime fleetmanagers may receive reports of ship positions and collect informationregarding the disposition of their own ships and their respectivecargos. However, this information is not largely publicly available andgenerally pertains only to specific vessels and is not associated withother data. Information regarding shipping traffic to and from variousports is typically gathered by port authorities and may be publiclyavailable, however such information is often limited in geographicscope.

A large number of variables that affect the global flow of commoditiesare not accounted for by present maritime data providers in a mannerthat allows interested parties to receive accurate updates regardingprojected arrival times for vessels and their cargos. For example,weather, political unrest, piracy, and even commodity pricing can causevessels to alter course and speed. Interested parties are currentlyforced to rely on anecdotal, untimely, spotty reports, and incompletemodeling for the data sets they require.

Heretofore known systems and methods for tracking commodity flows havegenerally been directed to acquiring tactical information and have beenlimited in geographic scope. Typical existing systems are static andbased on past ship movements, for example, but do not provide accurateinformation based upon current ship positions.

Heretofore known systems and methods for tracking commodity flows havefocused on acquiring information from only one mode of transportation(e.g., pipelines) or a limited number of transportation modes.

SUMMARY

What is needed is an improved set of components, methods and/orinfrastructure that incorporates various types of information foraccurately predicting worldwide flow of certain commodities involvingvirtually all shipping of those commodities around the world. Thisexpansive analysis and presentation system is not presently accessibleto the interested parties such as traders of the subject commodities oreconomists interested in global economic trends. Typical existingsystems do not provide an intermodal picture that combines data such astracking of seaborne commodities in transit with cargo informationcollected from other transportation modes (e.g., pipelines, freighttrains, helicopters, trucks, and airplanes). The present disclosureintroduces improvements to computerized systems that enable additionaldata to be collected and analyzed in such a way as to either infer orpredict various types of information with respect to commodity movementthroughout the world.

An illustrative example of the present disclosure provides a globalstrategic picture of commodity movements by tracking ships frommessaging such as AIS messaging, images and/or radar data from varioussources and then combining ship location and movement information with amultitude of other vessel, port, and cargo data sets (the terms ship andvessel are used interchangeably herein). Ship positions are integratedwith other data, such as vessel, port characteristics (berths, depth,location, cargo type, or any physical characteristic of the port),cargo, weather, and market information, to create a global strategicpicture of commodity flows. The global strategic picture providesdetailed commodity flow information to interested parties such ascommodities traders, freight traders, brokers, financial specialists,industry analysts, economists, supply chain managers, insurers,international financial markets, and governments. A global strategicpicture is generated by combining (i) ship movements gathered bysatellite and other sources, with (ii) vessel, port, cargo, weather,market, and other data from existing sources, and (iii) a time historyof these data sets.

In one aspect of this disclosure, the new components, algorithms andsystems disclosed herein can utilize received data to enable an improvedapproach of setting freight rates and optimizing or improving freightroutes. The algorithms can enable shippers to have a better sense ofpotential future revenue through strategic route planning which goesbeyond a current potential contract. In other words, the system canprovide suggestions on which contract to accept by a shipper not simplybased on the potential profitability of that current contract, but whatoverall profitability and efficiency can occur if a respective contractis accepted based on the following one or more contracts which can beobtained based on, for example, a destination port of the first contractand what additional contracts can be obtained for cargo from that portto third port, and so forth.

In another aspect, a method includes (1) receiving digital vessel datafor a global fleet of vessels, the digital vessel data being retrievedat least in part from one or more of AIS data, image data and/or radardata from various sources such as satellite, data transmitted byrespective vessels of the global fleet of vessels, land base antennas,or data generated to fill in gaps from data that are incomplete orinaccurate, (2) combining one or more of the digital vessel data,historical vessel location data, vessel location data, vessel physicalcharacteristics data, port physical characteristics data associated witha port and known patterns of maritime trade flows, to yield firstcombined data and (3) inferring, based on the first combined data, aloaded/empty status of at least one of a vessel or a type of cargo inthe vessel. The method can also include (4) combining, via theprocessor, one or more of the digital vessel data, the vessel locationdata, the historical vessel location data, the vessel physicalcharacteristics data, the port physical characteristics data, the typeof cargo and the loaded/empty status of the vessel to yield secondcombined data, (5) receiving data regarding one or more of supply,demand, and amount of available cargo to yield third combined data and(6) generating, based on one or more of the first combined data, thesecond combined data and the third combined data, information relatingto a supply of vessels available to load at a specified port and/ordeliver a cargo to a specified port, in each case within a specifiedperiod of time. Other steps can include generating suggestions for oneor more vessels regarding future routes based on one or more of thefirst combined data, the second combined data and the third combineddata or inferring the loaded/empty status of a plurality of vessels.

The digital vessel data can include two or more of satellite data, AISdata, image data and/or radar data received from one or more ofground-based receiver, satellite, or other data transmitted by theglobal fleet of vessels. The data can also include reconstructed orextrapolated data from incomplete or inaccurate vessel messages that arereconstructed or extrapolated according to a time-based analysis of theincomplete or inaccurate messages. The first combined data can include acombination of two or more of the digital vessel data, the historicalvessel location data, the vessel location data, the vessel physicalcharacteristics data, the port physical characteristics data and theknown patterns of maritime trade flows.

In one aspect of incomplete or inaccurate data, the system extrapolatesthe course of a vessel, as opposed to repairing the broken messages. Forexample, assume a vessel leaves the Gulf of Mexico and due to storms orsatellite location, the system does not hear from the vessel for threedays. When the system receives a message from that vessel, the systemcan proceed to fill in the missing location data where no messages werereceived for several days. Thus, the path of the vessel can beextrapolated from the data that was received.

In another aspect, messages received after leaving the Gulf of Mexico,the messages show the vessel in various locations throughout the world,even on land. The vessel is transmitting properly the messages but thesatellite mixed up the message, the system may ignore the messagesplacing the vessel on land by way of location. The data can beextrapolated data from incomplete or inaccurate vessel messages.

The port physical characteristics data can include one or more of anoperational status of the port, a position of the port, a capacity ofthe port, a size of the port, a number of berths within the port, alocation of the berths within the port, draft restrictions at the port,cargo handled by the port, and cargo handled at respective berths withinthe port. Any physical characteristic of the port can be utilized asport data.

Any of the principles disclosed herein can also equally apply to othervessel besides boats, such as trucks, trains, helicopters, drones,airplanes and so forth. Any vehicle or machine used to transport goodsis contemplated as within the scope of this disclosure. In thisscenario, the data such as the port data can apply to a trucking loadingdock or warehouse, and all of the physical characteristics associatedwith the loading dock or warehouse, and so forth. In another aspect, theconcepts disclosed herein can apply to supersets of data and can combinedifferent types of vessel data to provide an even more broad overview ofthe movement of cargo between ships, trucks, trains, helicopters,drones, airplanes and so forth.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the presentdisclosure will be more fully understood from the following detaileddescription of illustrative embodiments, taken in conjunction with theaccompanying drawings in which:

FIG. 1 is a system block diagram of a system for providing globalshipping and cargo information according to an illustrative embodimentof the disclosure;

FIG. 2 is a process flow diagram describing a system and method forproviding global shipping and cargo information according to aparticular embodiment of the disclosure;

FIG. 3 is a more detailed system block diagram of a system for providingglobal shipping and cargo information according to various illustrativeembodiments of the disclosure;

FIG. 4 is a flowchart of a method for generating a maritime trade andglobal fleet shipping activity report;

FIG. 5 is a flowchart of a method for inferring a course andidentification for a vessel;

FIG. 6 is a system diagram of an example computing system that mayimplement various systems and methods discussed herein, in accordancewith various embodiments of the subject technology;

FIG. 7 illustrates a method example for generating suggestions regardingfuture routes;

FIG. 8 illustrates a system example with various components that work toreceive data and process data to arrive at an inference of a loaded orempty or other status of a vessel;

FIG. 9 illustrates a method of handling mangled, incomplete, orinaccurate messages, such as MMSI messages, to retrieve missing data;

FIG. 10 illustrates a method of inferring a status of a vessel, cargotype, or other data about a vessel;

FIG. 11 illustrates another method of inferring a status of a vessel,cargo type, or other data about a vessel;

FIG. 12 illustrates another method of inferring a status of a vessel,cargo type, or other data about a vessel;

FIG. 13 illustrates another method of inferring a status of a vessel,cargo type, or other data about a number of different types of vessels,such as boats and trucks;

FIG. 14A illustrates a method embodiment; and

FIG. 14B illustrates another portion of the method embodiment of FIG.14A.

DETAILED DESCRIPTION

The present disclosure addresses the deficiencies in typical systems andintroduces new methods, computer systems, components and algorithms thatimprove the functioning of a computer system to provide newfunctionality not previously contemplated or implemented in priorsystems.

An illustrative embodiment of the present disclosure is described withreference to FIG. 1, in which a global strategic picture is generated bycombining (i) ship movements gathered by AIS messages, radar, imagesand/or other data from any source, with (ii) vessel, port, cargo,weather, market, and other data from existing sources and (iii) a timehistory of these data sets. Such other sources of ship movementinformation may include the Lloyd's Register database by Lloyd'sRegister—Fairplay Limited of Surrey, United Kingdom, the AISLivedatabase by AISLive Ltd., a United Kingdom-based company wholly owned byLloyd's Register—Fairplay Limited of Surrey, United Kingdom, the Lloyd'sMIU database by Lloyd's Maritime Intelligence Unit—Informa plc ofLondon, United Kingdom, the Clarksons database by Clarkson ResearchServices Limited of London, United Kingdom, and the Q88 or Baltic99databases by Heidenreich Innovations LLC, of Greenwich, Conn., U.S.A.,for example. Similar movement information can be obtained for truckmovement, train movement, drone movement, airplane movement, or anyother vehicle movement for products. All discussions about shipsdisclosed herein can also be applied to any vehicle or device that isused for moving goods from one place to another.

Attention is drawn to the terms “ship location data,” ‘vessel data,”“cargo data,” “port data,” “weather data,” and “market data.” Shiplocation data include, but are not limited to, International MaritimeOrganization (IMO) number, Maritime Mobile Service Identity (MMSI)number, vessel name, current latitude/longitude, heading, course, speed,and navigational status (e.g., anchored, underway). Ship location datamay be gathered by satellite-based Automatic Identification System (AIS)receivers, land-based AIS receivers, ship-based AIS receivers,Inmarsat-C GMDSS positions, Global Positioning System (GPS) positions,Long Range Identification and Tracking (LRIT) systems, ship-basedweather reporting, object-oriented analysis of high-resolution satelliteimages, ship location self-reporting, radar, other ship-based receivers,and market intelligence on vessel movements (e.g., oil tanker sightingsby port agents), as well as methods hereafter disclosed. One aspect ofthis disclosure is also dealing with incomplete or inaccurate messagesfrom any of these sources, and how to utilize other data to reconstructor extrapolate what the message data is for any particular type ofmessage. The system can also extrapolate a vessel path or predict avessel path based on a received message and previous data that is knownabout the vessel, such as a starting port.

Vessel data include, but are not limited to, as IMO number, MMSI number,vessel name, vessel type, tonnage, cargo type(s), cargo capacity, draft,age, owner, operator, charterer, length of charter, mechanical history,inspection history, certifications, previous ports of call, departuretime, loaded/empty status, expected port(s) of call, and estimatedtime(s) of arrival.

Port data include, but are not limited to, such information as cargotype(s), load/offload rates by cargo type or terminal, terminalcapacity, storage capacity, harbor congestion, navigational status(e.g., accidents restricting terminal access), draft restrictions,number of berths, draft restrictions, equipment available at a berth orloading dock, previous port cargo history, and terminal owner/managementcontact information.

Cargo data include, but are not limited to, type of cargo (e.g., crudeoil), subtype of cargo (e.g., grade of crude oil), amount of cargo in astorage facility, amount of cargo loaded on a vessel, broker data oncharter fixtures, bills of lading, cargo manifests, certificates oforigin, certificates of quality and quantity, master's receipt ofsamples, US Customs data, customs data from other countries, and tariffdata.

Weather data include, but are not limited to, weather reports, weatherforecasts, and information on rainfall, hurricanes, typhoons, tropicalstorms, tsunamis, and other severe weather events. Market data include,but are not limited to, commodity prices, spot market prices, futuresprices, options prices, information on swaps, information onderivatives, supply or expected supply of certain commodities, demand orexpected demand of certain commodities, information from exchanges(e.g., NYMEX), information from over-the-counter (OTC) trades,chartering rates, freight rates, economic data, economic trends, worldtrade data, export data, import data, security risks, marketintelligence, market news, and market updates. Economic, trade, export,and import data are available at the local, state, national, regional,and/or international levels, and from public sources (e.g., officialstatistics) and/or private sources (e.g., data services provided byprivate companies, such as Bloomberg, IHS Global Insight, etc.)

With regard to loaded/empty status and cargo data, attention is alsodrawn to the term “likely,” which may mean about 70% or greater accuracywhen data are aggregated over a one-year time period. The loaded/emptystatus of the vessel/vehicle can also include a probability or a likelyload amount, such as 50% full or completely fully loaded. For example,depending on vessel draft, and based on the inferred cargo type, theconclusion of the system might be that the ship is 80% full of cargo.

The illustrative embodiment of FIG. 1 includes a system 100 forproviding global shipping and cargo information. The system 100 includesat least one vessel 102 having a position reporting device and at leastone satellite 104 receiving vessel position information from theposition reporting device or at least one land-based receiver 103receiving vessel position information from the position report device.The system also includes at least one data center 106 receiving thevessel position information from the satellite 104 via a communicationsystem 108 or receiving the vessel position information from theland-based receiver 103. The data center 106 combines the positioninformation with at least one ship information database 110 and at leastone ancillary database (e.g., port, cargo, weather, and market data) 109to generate a global strategic picture 112 of the global shipping andcargo information. The system also includes a user computing device 114in communication with the data center 106. The user computing device 114receives the global strategic picture 112 from the data center.

An illustrative implementation of the present disclosure is describedwith reference to FIG. 2 in which ship position information is receivedas AIS information from a low earth orbiting satellite (202). Shipgeospatial information is determined by other satellite means such as asatellite that permits voice communications using a single uplinkfrequency on one amateur band and a single downlink frequency on anotheramateur band known as “bent pipe” from satellite communications, GPS,LRIT systems, and object-oriented analysis of high resolution satelliteimages (204). Ship position information is also received as AISinformation from land-based AIS networks, such as the AISLive databaseby AISLive Ltd., a United Kingdom-based company wholly owned by Lloyd'sRegister—Fairplay Limited of Surrey, United Kingdom (205). The variousreceived signals are then integrated into a global picture of everyvessel larger than 300 gross tons (206). The ship data are thenintegrated with various other relevant data sets such as vessel cargocapacity, cargo type, amount of cargo, port data, previous ports ofcall, port and terminal data, commodity prices, weather, portcongestion, data about other cargo carriers such as truck, trains,helicopters, drones, airplanes, image data, or radar data associatedwith any vehicle or vessel associated with the cargo, reconstructed orextrapolated message data, or extrapolate or reconstruct a vessel path,etc. (208). The illustrative implementation includes a computercomponent for sorting world fleet information at once by cargo type,ship size, or vessel type; a computer component to determine likelyvessel loaded/empty status, likely cargo type and subtype, and likelyamount of cargo with rules-based logic (e.g., particular ports arepoints of transfer for specific cargo, time a ship is located at a portof call as an indicator of whether there was time to fully or partiallyload a ship), a computer component configured to aggregate individualvessel data into categories such as vessel type, cargo capacity, andloaded/empty status, means to categorize global cargo flows by commoditytype and subtypes, such as individual grades of crude oil, a computercomponent configured to provide average vessel speed for particularships in any given body of water; and a computer component configured topresent a real-time picture and an historic picture of geographicproximity of a world fleet relative to a particular port, shipping lane,sea route, or transit point (210).

An illustrative embodiment of a global strategic picture can be thoughtof as a dynamic “data cube” with three axes—X-axis, Y-axis, andZ-axis—producing useful combinations of data moving through time. TheX-axis of the data cube includes vessel, port, cargo, and other datafrom existing sources. These data may come from existing sources such asthe Lloyd's Register, Lloyd's MIU, Clarksons and Q88 databases. TheY-axis of the data cube includes ship location data. These data willcome from satellite sources, such as ORBCOMM and COM DEV, and otherland-based sources, such as AISLive or image or radar data from sources.In this example, the Z-axis of the data cube represents time.

The time history of ship movements and cargo information (includinglikely cargo information) is useful to create a record of commodityflows, allowing for statistical trend analysis. This is a usefulcontribution in part because one can study global commodity movements inhindsight, using data that is global in scope and comprehensiveness.This may contribute to all kinds of analyses, including how temperatureswings, changes in economic conditions, changes in world trade, andgeopolitical events affect the production, transportation, andimportation of commodities, such as crude oil. This trend analysis willafford new insights into how global economies interact with each otheras well as market intelligence into how economies will respond toshocks, disruptions, or other pressures in contrast to past observedglobal commodity movements. This statistical analysis can be bothquantitative and qualitative, looking for micro- and macro trends basedon the first worldwide data archiving of observed global fleetmovements. These trends can extend into the land component of acommodity such as corn where seeding, harvesting, storing, shipping viatruck or train to a ship can all be analyzed as well to evaluate,predict, infer and guide the movement of commodities.

In one embodiment, subscribers may access these data through a web-baseduser interface and/or via an existing distribution network such asReuters, Bloomberg, or PIRA Energy Group, for example. Subscribers canset parameters and filters to organize and search the data over auser-defined time period (e.g., based on the start of the trading dayfor their location, bi-daily, hourly, etc.). Users can generatevalue-added outputs such as the average speed of the crude oil tanker orLNG (liquefied natural gas) carrier fleet, how weather affectsmacro-ship movements, the physical location of all crude oil tankers orLNG carriers vis-à-vis spot markets, a macro-picture of port congestion,and market intelligence on time spreads between futures contracts fordifferent months, value spreads between futures contracts for differentgrades of crude oil, OPEC exports of crude oil, non-OECD imports ofcrude oil, edge on EIA and OECD official statistics, and early notice onsupply shocks or diversions of tankers between markets. The userinterface software may present data in numerous formats such as (i) viaa web-based interface, (ii) downloaded data presented in a spreadsheetuser interface, such as Microsoft Excel, (iii) geospatially formatteddata for a user interface such as Google Earth or Google Maps, and/or(iv) a live data feed.

One primary data source for ship geospatial information according toillustrative embodiments of the disclosure includes satellite receptionof AIS transmissions from individual ships. ORBCOMM has installed AISreceivers on their newest constellation of low earth orbitingsatellites. COM DEV has an existing AIS satellite. Additional AISsatellites are likely to be available soon. As presently configured, AISdata provides a vessel-specific IMO number, a vessel-specific MMSInumber, a vessel call sign, and dynamic information from the ship'snavigation systems including current latitude/longitude position,course, speed, destination, estimated time of arrival, previous ports ofcall, and navigational status (e.g., anchored). While AIS transmissionswere originally intended for reception by local ground-based stations,reception of these transmissions by satellite according to illustrativeembodiments of the present disclosure provides an improved method ofmaritime data collection for ships anywhere on earth.

In one aspect, this disclosure provides for an method of reconstructingor extrapolating messages, such as an MMSI message or AIS message, thatis corrupted, incomplete or inaccurate by evaluating or predicting thetime the message was sent, and coordinating that data with knownshipping position data or vehicle position data to infer or identify thesource vessel and thus fill in missing gaps of data over time. In thisway, a previously useless and discarded message can now contain valuableinformation and be used in the analysis. The system can also utilize amessage to reconstruct or extrapolate a vessel path where messages whichwould have indicated the path are missing or corrupted.

Another data source for ship geospatial information according toillustrative embodiments of the disclosure include input from other shippositional data sources such as Inmarsat-C GMDSS positions, GPSpositions, LRIT systems, ship-based weather reporting, object-orientedanalysis of high-resolution satellite images, ship locationself-reporting, radar, land-based AIS receivers, such as the AISLivenetwork, ship-based AIS receivers, other ship-based receivers, andmarket intelligence on vessel movements (e.g., oil tanker sightings byport agents), among other sources. The shipping location informationfrom various sources is then incorporated with a multitude of other datasets to create a new global picture of commodity flows.

Other data sets that can be incorporated with ship location informationaccording to various embodiments of the disclosure include, but are notlimited to, vessel, port, cargo, weather, and market data. Data can beaggregated for each combination of commodity type, ports of call, andship type. Variance and standard deviation of each data field at theship and aggregated level is also provided.

A particular embodiment of the disclosure which combines various datasources is described with reference to FIG. 3. Location data such as AISdata 301, Inmarsat C data 302, GPS or ship transponder data 303, LRITdata 304, and air traffic control data 316 is communicated to a firstdatabase 305. Location data takes the form of latitude/longitude data,which is linked to a specific vessel using a unique vessel identifier,such as MMSI number or IMO number. Vessel data, cargo data, and portdata 306 such as Lloyd's Register—Fairplay ship information, Lloyd's MIUship information, Clarksons ship information, Q88 and Baltic99 shipinformation, port and terminal data (e.g., location, cargo types, cargoload/offload rates), cargo manifest data, bills of lading data,commodity prices, air traffic control data and other port data is alsocommunicated to the first database 305. The first database is scrubbedfor data consistency and errors. Field formats are checked, standardizedand de-duplicated. The first database is then processed into hourly anddaily logs. Rules-based logic determines likely “empty” or “full” statusof each vessel, and matches likely cargo data to each vessel. Apartially full status can also be used. A global strategic picture (GSP)processor 308 combines data from the first database 305 with userdefined data 307 such as commodity type information, route information,and region information to create a customized global strategic picture(GSP). The GSP is then stored in a GSP database 309 and can be accessedby a GSP trend processor 310. The GSP trend processor can create acustomized GSP trend by processing GSPs over a user-defined time period.The GSP trend can then be stored in a GSP trend database 311. The GSPdatabase 309 can also be accessed by a presentation processor 312 whichpresents the GSPs to various application front ends. Such applicationsinclude commodity view applications 313, government and securityapplications 314 and fleet management applications 315.

Embodiments of the disclosure provide commodity prices at variousmarkets around the world and may be provided to users through commodityview applications 313. For example, LNG is currently traded in fourmarkets: North American, European, NE Asian, and SE Asia. Thisdisclosure will provide current spot market prices and futures marketprices for a variety of commodities in various markets around the world.This supplements the global strategic picture of commodity movements.Embodiments of the present disclosure may provide a comprehensivereal-time, or near real-time, global strategic picture of commoditymovements that is constantly updated and captures the dynamic nature ofinternational shipping.

Embodiments of the disclosure can provide a real-time, as well ashistorical, global picture of world trade patterns and trends to, forexample, a user computing device 114. This will provide data on local,state, national, regional, and international exports and imports inadvance of available public sources (e.g., the release of officialstatistics) and/or private sources. This will be particularly valuableto economists, industry analysts, and equity researchers who specializein understanding and predicting global economic trends and world tradepatterns ahead of the market. For example, this will provide an earlyindication of which countries are experiencing significant increases ordecreases in export and/or import volumes. Embodiments of the disclosurewill also be valuable because the world trade and economic data will becollected using a different methodology than current sources (e.g.,statistics gathered using surveys and interviews).

Embodiments of the disclosure may also provide software that allows auser to select a kind of cargo or product carried aboard ship totrack/see. This is especially valuable for financial transactions suchas trading, futures, derivatives, etc. on especially two kinds ofcargo: 1) “wet bulk” such as crude oil, refined petroleum products,chemicals, etc. and 2) “dry bulk” such as agricultural products, metals,coal, steel, etc., although it would not be limited to these cargo typesalone.

As non-limiting examples, embodiments of the disclosure will usefullyconsider the following vessel types to create a global or regionalstrategic picture of cargo flows, categorized by cargo type or vesseltype: LNG carriers, liquefied petroleum gas (LPG) carriers, ethylenecarriers, very large crude carrier (VLCC) tankers, ultra large crudecarrier (ULCC) tankers, Suezmax tankers, shuttle tankers, Panamaxtankers, Aframax tankers, handysize tankers, wine tankers, fruit juicetankers, water tankers, sulfuric acid tankers, phosphoric acid tankers,palm oil tankers, methanol tankers, m. sulfur tankers, m. phosphorustankers, edible oil tankers, asphalt & bitumen tankers, bauxite bulkers,cement bulkers, chip bulkers, forest product bulkers, gypsum bulkers,limestone bulkers, lumber bulkers, ore bulkers, pipe bulkers, stone chipbulkers, etc. Also any kind of tuck, train, drone, airplane or any othervessel will be taken into account.

Certain ships/vehicles carry multiple cargoes. This disclosure resolvesthat issue by monitoring the time each vessel spends at each port, andmatching that with the cargo type of that port and the load/offload ratethrough ship information databases 110, ancillary database 109containing port and cargo information, or a combination thereof. Othersources of cargo information for multi-cargo vessels include broker dataon charter fixtures, bills of lading, vessel self-reporting, andpersonal communications with individual vessels, their owners, oroperators all of which may be accessed by the system 100.

Embodiments of the disclosure provide an abstract view of the globalsupply curve at any point in time for each combination of commodity typeor types, port or ports of call, ship type or types, and date range,which may be stored in, for example, the first database 305. This willbe valuable information for commodities traders, brokers, freighttraders, industry analysts, economists, and other financial specialists,as well as owners, shippers, ship managers, port operators, supply chainmanagers, insurers, and others in the shipping business who couldbenefit from increased transparency in spot markets and futures marketsfor commodities, such as crude oil, natural gas, refined petroleumproducts, aluminum, copper, iron ore, lumber, etc.

Embodiments of the disclosure may use rules-based logic, Bayesian logic,neural networks, learning algorithms, or other mathematical methods tointegrate (i) data on vessel location for many or substantially allvessels in the world fleet, and (ii) data on vessel type, vessel cargocapacity, cargo type, and vessel tonnage with (iii) likely loaded/emptystatus and likely amount of loaded/offloaded cargo, to create a globalstrategic picture of commodity movements. The global picture can alsoenable the forecast of shipping rates for ships or vehicles, and suggestwhat contracts a vehicle or vessel should accept based on projectedfuture earnings or efficiency of the next or one or more future shippingcontracts. Unique ship identifiers, such as MMSI numbers and IMOnumbers, allow for integrating, aggregating, and filtering data byvessel location, vessel type, vessel cargo capacity, vessel tonnage,cargo type, likely amount of cargo, and likely loaded/empty status.

Embodiments of the disclosure use a rules-based logic to determinelikely “loaded” or “empty” status for an individual vessel, based onthat individual vessel's previous ports of call or another vesselengaged in lightering activities. For ports, likely loaded/empty statuscan be determined by matching vessel location data with port locationdata over time. If a cargo vessel spends more than X number of hours ata certain loading-berth, then the rules-based logic designates thatvessel as “loaded” when it departs that loading-berth. The system canalso infer or predict if it is partially loaded, such as 80% capacitybased on cargo and how much time the ship was at the berth, and/or otherfactors. If a cargo vessel spends more than X number of hours at acertain offloading-berth, then the rules-based logic designates thatvessel as “empty” when it departs that offloading-berth. In otherembodiments “loaded” or “empty,” or likely “loaded” or “empty,” statuscan be determined by such methods as Bayesian logic, neural networks,learning algorithms, other mathematical methods, direct inquiry toowners, shippers or port personnel or by historic data (e.g., scheduledshipping) or additional contextual or inferential data (e.g., season,port, type of ship, market conditions etc.).

For example, if an LNG vessel stops for more than 6 hours at an LNGloading-berth in Qatar, the rules-based logic designates that LNG vesselas “loaded” when it departs that loading-berth. Similarly, if an LNGvessel stops for more than 6 hours at the LNG offloading-berth inEverett, Mass., the rules-based logic designates that LNG vessel as“empty” when it departs that offloading-berth. Again, a finergranularity can also be provided in which a percentage of a full loadcan also be predicted.

The “loaded/empty status” rules-based logic combines the staticlatitude/longitude information of the loading- and offloading-berth,with the dynamic latitude/longitude position information for eachvessel. Whether or not stated as “likely,” the potential inferentialstatus of such designations is acceptable for the practice of thisdisclosure.

Lightering involves a larger vessel offloading cargo on to a smallervessel because of draft restrictions in a nearby port of call. Forvessels engaged in lightering activities, loaded/empty status isdetermined by matching location data for the larger vessel with thelocation data for the smaller vessel over time. If a smaller vesselspends more than X number of hours (a number based on factors such asknown or estimated capacity or displacement) alongside a larger vessel,then the rules-based logic designates the smaller vessel as “loaded”with the same cargo type as the larger vessel had.

Embodiments of the disclosure use each vessel's unique identifier (e.g.,MMSI number) to match “loaded/empty status” with vessel data, such asvessel type, vessel cargo capacity, and vessel tonnage.

In addition to using the latitude/longitude points (or other globalpositioning reference points) for a certain loading- andoffloading-berth, the “loaded/empty status” rules-based logic can use apre-defined geographic area to determine the applicable loading- andoffloading-berth. For example, the rules-based logic can use a proximityfigure such as a 10-mile radius from a certain latitude/longitude pointto define an expanded geographic area for an loading- andoffloading-berth or another vessel engaged in lightering. After a vesselspends a minimum amount of time within that 10-mile radius, therules-based logic determines loaded/empty status for that vessel.

When a vessel makes multiple ports of call at crude oil loading orunloading berths, “loaded” status may be represented by a percentage(e.g., 60% loaded), as noted above.

Embodiments of the disclosure use a rules-based logic that combines timespent at a certain loading- and offloading-berth or another vesselengaged in lightering with the load/offload rate of cargo to determinethe likely amount of cargo loaded/unloaded at the loading- andoffloading-berth or another vessel engaged in lightering. For example,if a crude oil tanker spends six hours at a crude oil loading-berth witha 10,000 barrel per hour load rate, the rules-based logic calculatesthat 60,000 barrels of oil were likely loaded on that tanker.

When a vessel makes multiple ports of call at crude oil loading- oroffloading-berths, or vessels engaged in lightering, the load/offloadrates at those berths can be used to determine the likely percentage“loaded” status of that vessel (e.g., 60% loaded). For example, if acrude oil tanker leaves a crude oil loading-berth in Saudi Arabia 100%loaded and offloads oil for 6 hours at a crude oil offloading-berth inSingapore on the way to delivering the rest of its crude oil at animport terminal in Ningbo, China, then the offload rate at the Singaporeterminal can be used to calculate the likely remaining percentage of oilgoing to the Ningbo terminal.

Embodiments of the disclosure use cargo information for loading-berthsto determine what specific type of cargo is likely loaded on a vessel.Certain loading-berths only export a certain type of a given cargo(e.g., a specific grade of crude oil). For example, if a crude oiltanker loads crude oil at Bonny Terminal in Nigeria, one can infer thatthe crude oil tanker has loaded Bonny Light crude oil because BonnyLight is the only crude oil exported from Bonny Terminal in Nigeria.This more detailed cargo information is valuable to crude oil tradersbecause various grades of crude trade at different prices in commodityand futures markets.

It is to be appreciated that the properties of certain cargos may beconsidered in calculations of how much cargo is likely being carried bya particular vessel. For example, different grades of crude oil havedifferent weights. Heavier grades of crude take more cargo space incrude oil tankers than lighter grades do and, thus, require a differentcalculation to convert cargo capacity from dead weight tons to barrelsof oil. Rules-based logic, accounting for the different weights for eachgrade of crude, will calculate how much cargo or the maximum possibleamount of a particular cargo that is likely aboard a particular vessel.In performing such calculations we make note of API gravity, a specificgravity scale developed by the American Petroleum Institute measuringthe relative density of various petroleum liquids, expressed in degrees.

Embodiments of the disclosure use rules-based logic, Bayesian logic,neural networks, learning algorithms, or other mathematical methods toproduce a useful estimate of how much of a certain type of cargo isbeing exported from a defined set of loading-berths (aggregatingshipments of that specific cargo departing those loading-berths) over adefined time period, and track each cargo shipment over time to show thedestination offloading-berth. For example, a rules-based logic allows auseful determination of aggregate crude oil exports from loading-berthslocated within Organization of Petroleum Exporting Countries (OPEC)countries over a preceding two months, and can include quantitative dataon deliveries to destination offloading-berths. These crude oil exportdata can then be compared to the official or other published statistics.In some instances, the concordance or disparities in data will offeruseful market information both as to the volume of shipments and theaccuracy of the various reports. Having an accurate picture of crude oilexports and imports as well as an “audit” assessment as to data sourcesprovides interested parties with useful information, including supplyindicia that may impact spot and futures prices of crude oil.

Embodiments of the disclosure use a rules-based logic, Bayesian logic,neural networks, learning algorithms, or other mathematical methods toproduce a useful estimate of how much of a certain type of cargo isbeing imported into a user-defined set of offloading-berths (aggregatingshipments of that specific cargo arriving at those offloading-berths)over a defined time period, and trace the historical track of each cargoshipment to show the origin loading-berth. For example, a rules-basedlogic allows a useful determination of aggregate crude oil imports intooffloading-berths located within India and China over the last twomonths, and traces the historical track of those crude oil shipments totheir origin loading-berths. These crude oil import data can then becompared to the official or other published statistics.

Embodiments of the disclosure analyze the height of vessels above waterto estimate how much of a certain type of cargo is on board the vessel.Vessels laden with cargo sit low in the water, while vessels in ballastsit high in the water. Rules-based logic, Bayesian logic, neuralnetworks, learning algorithms, or other mathematical methods may be usedto estimate the amount of cargo in a specific vessel at a certain time,given that vessel's individual specifications and its current heightabove water. Vessel height above water can be detected by satellite,land-based, sea-based, or air-based surveillance systems, includingremote sensing or visual observations by humans (e.g, by harbor mastersor port agents), and web cameras in ports or other locations.

Useful data by the process of this disclosure is also developed withlimited the end-user output to certain data fields, such as the location(e.g., latitude/longitude) and amount of cargo in transit worldwide fora certain commodity type, such as crude oil. Particular note is made ofdata including unique vessel identifiers, such as MMSI numbers and IMOnumbers, to integrate (i) vessel location data from satellite-based orland-based AIS networks or image or radar information, (ii) vessel data,(iii) loaded/empty status, cargo type, and amount of cargo from previousports of call, and (iv) cargo, weather, market, and other data frombrokers, charterers, shipowners, cargo manifests, bills of lading, andmarket intelligence. These data are then usefully aggregated worldwideand categorized by vessel type, likely cargo type, and likelyloaded/empty status to show all cargo in transit for a certain cargotype, such as crude oil, but without providing individual vessel namesor other vessel-specific data to end-users. Similarly, this dataaggregation and categorization can show all available tonnage for acertain vessel type, such as crude oil tankers, but without providingindividual vessel names or other vessel-specific data to end-users.

The foregoing functionality is useful in instances where security is aconcern in offering market information without inclusion of sensitivevessel-specific information.

For example, an embodiment of the disclosure uses MMSI numbers to createa global picture of crude oil flows carried by likely “loaded” crude oiltankers. This involves using MMSI numbers to integrate (i) vessellatitude/longitude data for crude oil tankers received fromsatellite-based and land-based AIS networks, (ii) cargo capacity andvessel tonnage data for crude oil tankers from several sources,including Lloyd's Register—Fairplay, Lloyd's MRJ, Clarksons, and Q88,(iii) loaded/empty status for crude oil tankers based on previous portsof call, crude oil grade data based on last crude oil loading-berth, andamount of crude oil cargo based on time spent at last crude export oroffloading-berth, and (iv) cargo, weather, market, and other crude oildata from brokers, charterers, shipowners, cargo manifests, bills oflading, and market intelligence. These data are then aggregatedworldwide for all crude oil tankers and categorized by likelyloaded/empty status, likely amount of crude oil on board, and likelycrude oil grade to show all crude oil in transit worldwide. Thisembodiment of the disclosure records these data in a time history. Thisglobal picture of crude oil flows does not provide individual vesselnames or other vessel-specific data to end-users, but remains valuablefor crude oil traders, natural gas traders, refined products traders,freight traders, and other traders who trade commodities that areinfluenced by crude oil movements. This embodiment of the disclosureinvolves one or more of the following steps:

(1) Use MMSI Number filter to limit the AIS data from the world fleet toonly crude oil tankers.

(2) Use rules-based logic, Bayesian logic, neural networks, learningalgorithms, or other mathematical methods to determine likely “loaded”status for each crude oil tanker (unique MMSI Number) whose previousports of call was a crude oil loading-berth, and the likely crude oilgrade loaded at that crude oil loading-berth. When a crude oil tankermakes multiple ports of call at crude oil offloading-berths, likely“loaded” status may be represented by a percentage (e.g., 60°/a loaded).

(3) Integrate the cargo capacity of each “loaded” crude oil tanker(unique MMSI Number) from the cargo capacity data from sources such asLloyd's Register—Fairplay, Lloyd's MIU, Clarksons, and Q88. Thisinvolves matching likely “loaded” status with cargo capacity for eachcrude oil tanker (same unique MMSI Number).

(4) Use rules-based logic, Bayesian logic, neural networks, learningalgorithms, or other mathematical methods to determine the likely amountof crude oil loaded/offloaded on each “loaded” crude oil tanker (uniqueMMSI Number) by combining time spent at a certain crude oil loading- andoffloading-berth with the likely load/offload rate of crude oil at thatloading- and offloading-berth.

(5) Use MMSI Numberto integrate the AIS data, includinglatitude/longitude information, for each “loaded” crude oil tanker(unique MMSI Number) with the crude oil cargo data for that crude oiltanker (same unique MMSI Number).

(6) Aggregate the above to show latitude/longitude and likely amount ofcrude oil cargo for each “loaded” crude oil tanker. Each vessel-specificdata combination receives a time stamp.

(7) Aggregate these vessel-specific data combinations to show thelatitude/longitude and likely amount of crude oil cargo for all “loaded”crude oil tankers worldwide.

(8) Record the time history of this aggregated picture (likely loadedstatus, latitude/longitude information from AIS data, cargo capacity,and likely amount of crude oil on board).

(9) Provide end-users with a global picture of crude oil flows,including a time history, without disclosing individual vessel names orother vessel-specific data.

Similarly, for example, an embodiment of the disclosure uses MMSInumbers to create a global picture of available crude oil tanker tonnagefrom “empty” crude oil tankers. This global picture of available crudeoil tanker tonnage need not provide individual vessel names or othervessel-specific data to end-users, but remains valuable for freighttraders and other traders who trade commodities that are influenced byavailable tanker tonnage. This embodiment of the disclosure involves oneor more of the following steps:

(1) Use MMSI Number filter to limit the AIS data from the world fleet toonly crude oil tankers.

(2) Use rules-based logic, Bayesian logic, neural networks, learningalgorithms, or other mathematical methods to determine the likely amountof crude oil offloaded from each crude oil tanker (unique MMSI Number)by combining time spent at a certain crude oil offloading-berth with thelikely offload rate of crude oil at that loading- and offloading-berth.

(3) Use rules-based logic, Bayesian logic, neural networks, learningalgorithms, or other mathematical methods to determine likely “empty”status for each crude oil tanker (unique MMSI Number) whose previousports of call was a crude oil offloading-berth, and whose amount ofcrude oil offloaded at its various stops at offloading-berths is withina threshold of that vessel's cargo capacity. Cargo capacity data can befrom sources such as Lloyd's Register—Fairplay, Lloyd's MIU, Clarksons,and Q88, or may be inferred or calculated from aggregate resources.

(4) Integrate the available vessel tonnage of each likely “empty” crudeoil tanker (unique MMSI Number) from the vessel tonnage data fromsources such as Lloyd's Register—Fairplay, Lloyd's MIU, Clarksons, andQ88. This involves matching likely “empty” status with vessel tonnagefor each crude oil tanker (same unique MMSI Number).

(5) Use MMSI Number to integrate the AIS data, includinglatitude/longitude information, for each “empty” crude oil tanker(unique MMSI Number) with the vessel tonnage for that crude oil tanker(same unique MMSI Number).

(6) Aggregate the above to show latitude/longitude and likely amount ofavailable crude oil tanker vessel tonnage for each “empty” crude oiltanker. Each vessel-specific data combination receives a time stamp.

(7) Aggregate these vessel-specific data combinations to show thelatitude/longitude and amount of available crude oil tanker vesseltonnage for all likely “empty” crude oil tankers worldwide.

(8) Record the time history of this aggregated picture (likely emptystatus, latitude/longitude information from AIS data, and likelyavailable crude oil tanker vessel tonnage).

(9) Provide end-users with a global picture of available crude oiltanker vessel tonnage, including a time history, without disclosingindividual vessel names or other vessel-specific data.

Embodiments of the disclosure provide a global picture of commodities instorage on vessels, such as crude oil being stored in oil tankers andmotor vehicles being stored in pure car carriers. For crude oil, thisphenomenon is referred to as floating storage. Floating storage tends toincrease when crude oil prices are low and/or land-based crude oilstorage facilities are at capacity or not available. Information onfloating storage is valuable to crude oil traders, natural gas traders,refined products traders, freight traders, and other traders who tradecommodities that are influenced by crude oil movements, because havingan accurate picture of crude oil storage provides interested partieswith useful information, including supply indicia that may impact spotand futures prices of crude oil.

Embodiments of the disclosure integrate sea routes into the geographicalcalculation of distances from vessels to ports. Sea routes can bepre-defined using standard preferred sea routes (e.g., the Suez Canalroute from Asia to Northern Europe, which transits the China Seas,Malacca Strait, Indian Ocean, Gulf of Aden, Red Sea, Mediterranean Sea,and English Channel) or user-defined sea routes. Sea route calculationscan be integrated from existing sources, such as the sea route softwareprovided by AtoBviaC Plc (Berkhampstead, Hertfordshire, United Kingdom),or calculated manually and added to the rules-based logic thatcalculates distance from vessels to ports. The integration of sea routesinto embodiments of the disclosure allow for more accurate calculationsof transit time for an individual vessel or cargo movement to possibledestination ports, from port of origin, or to or from other ports ofinterest.

Embodiments of the disclosure use rules-based logic, Bayesian logic,neural networks, learning algorithms, or other mathematical methods toimpute the possible destination ports of a vessel by using vessellocation, course, and speed, and by filtering possible destination portsby cargo type, vessel type, or loaded/empty status. For example, if acrude oil tanker is located in the North Atlantic, rules-based logic,Bayesian logic, neural networks, learning algorithms, or othermathematical methods can filter crude oil offloading-berths out of allthe ports in the North Atlantic, calculate distances to each possibledestination offloading-berth, and integrate relevant historicalinformation (e.g., number of times the said crude oil tanker has calledat each of the possible destination offloading-berths) to impute thelikely destination offloading-berth. Rules-based logic, Bayesian logic,neural networks, learning algorithms, or other mathematical methods canalso sort the possible destination ports according to the estimatedprobability of the individual vessel or cargo movement calling at eachpossible destination port.

If the ship location data is only available in irregular time intervalsfor a certain vessel, embodiments of the disclosure extrapolate thehistoric path of that vessel by connecting the dots between the shiplocation data from the two most recent signals. Thresholds are definedso that the extrapolation function does not go awry if incorrect orcorrupted ship location data is transmitted. Any message can be used toreconstruct the historical vessel path.

Embodiments of the disclosure allow ship location data, vessel data,port data, cargo data, and other data (such as weather and marketinformation) to be sorted geographically by port(s), country orcountries, ocean basin(s), port pairs, country pairs, ocean basin pairs,sea route(s), and key transit points. Geographical parameters arecapable of being set for each of the following categories:

Port(s): Sorting by port provides users with export/import informationfor an individual port or a set of ports.

Country or countries: Sorting by country provides users withexport/import information for an individual country or a set ofcountries. For example, commodity traders can determine the aggregateoil exports from members of the Organization of Petroleum ExportingCountries. Similarly, for example, one can determine the aggregate oilimports to a user-defined set of countries, such as India, China, andSouth Korea.

Ocean basin(s): The geographic areas of certain ocean basins, such asthe Baltic Sea, the Mediterranean, the Arabian Gulf, the North Atlantic,the North Pacific, and the Indian Ocean are defined. This would allowusers to assess vessel/cargo flows within an ocean basin or set of oceanbasins (e.g., within the Baltic Sea).

Port pairs: Users can assess vessel cargo flows between two or moreports (e.g., from Das Island, United Arab Emirates to Everett, Mass.).

Country pairs: Users can assess vessel/cargo flows between two or morecountries (e.g., from Russia to Canada).

Ocean basin pairs: Users can assess vessel/cargo flows between two ormore ocean basins (e.g., between the Arabian Gulf and the North Sea).

Sea route(s): The geographic areas of certain sea routes, such as thetrans-Pacific route, the trans-Atlantic route, and the Asia-to-Europeroute are defined. This allows users to assess vessel cargo flows alongcertain sea routes (e.g., along the Great Circle Route in the PacificOcean).

Key transit points: The geographic areas of certain sea routes, such asthe Suez Canal, the Panama Canal, the Malacca Straits, the Strait ofGibraltar, the Bosporus, the English Channel, the Cape of Good Hope, andCape Horn are defined. This allows users to assess vessel cargo flowsthrough certain key transit points (e.g., the Suez Canal)]

Embodiments of the disclosure can be used by freight traders who tradeon the availability of merchant vessels. The freight traders areprovided with data on the supply of likely empty (“in ballast”) vesselsin a certain geographical area, such as two days away from Port X (basedon average speed and course of each individual vessel). These data canbe sorted by ocean basin, such as the North Atlantic or South China Sea.Using filters, freight traders can sort likely empty vessels usingcategories such as vessel type, vessel tonnage, vessel cargo capacity,and vessel age.

The supply data of likely empty vessels is combined with other data onvessel availability—such as ship owner, ship charterer, length ofcharters—to give freight traders information on the available supply oflikely empty vessels. Using filters, freight traders can sort availablelikely empty vessels using categories such as vessel type, vesseltonnage, vessel cargo capacity, and vessel age. This can be used topredict freight rates and suggest the optimal or improved utilization ofthe vessel taking into account the immediately following voyage andvoyages that might follow afterward.

In addition to providing freight traders with data on the supply andlocation of currently empty merchant vessels, analytics can be used toassess when a likely loaded (“laden”) vessel would be able to reach adischarge port, unload its cargo, and return to a certain port or oceanbasin in X days (based on average speed and historic routes ofindividual vessels). These data on individual likely loaded vesselscould be aggregated to give a picture of the future supply of emptymerchant vessels. For example, if a freight trader wants to trade on theavailability of VLCC oil tankers in the Port of Jeddah, Saudi Arabia in30 days, one can calculate which likely loaded merchant vessels coulddischarge their cargo in ports such as the Port of Rotterdam and, basedon their average speed, could reach the Port of Jeddah within 30 days.Using filters, freight traders can sort such vessels using categories,such as vessel type, vessel tonnage, vessel cargo capacity, and vesselage.

The supply data of likely loaded vessels can be combined with other dataon vessel availability—such as ship owner, ship charterer, length ofcharters, chartering rates, and freight rates—to give freight tradersinformation on the available supply of laden vessels, and then use thatinformation to calculate the future availability of empty merchantvessels. Using filters, freight traders can sort such vessels usingcategories such as vessel type, vessel tonnage, vessel cargo capacity,and vessel age.

When using filters, embodiments of the disclosure can also be used as aglobal fleet management tool. Such embodiments allow ship owners,management companies, shipping lines, etc. to track their worldwidefleets in real-time as well as forecast freight rates and optimize theefficient use their fleet in the future.

Embodiments of the disclosure are also is useful for port planning. Theglobal strategic picture of commodity flows can help ports manage theiroperations and make more informed infrastructure investments as theywould be able to see the actual shipping and cargo flows passing neartheir port.

Shippers and logistics companies focused on global supply chainmanagement can use embodiments of the present disclosure to match theirsupply chain data with the global strategic supply database. This allowsembodiments of the disclosure to incorporate at least part of theworld's container fleet into the database. Many shippers are pursuingtotal supply chain visibility so they always know the location of theirproducts. They use GPS transponders, RFIDs, etc. to track containerscarrying their products. However these technologies do not work when thecontainer is buried 30 boxes down in transit across the ocean becausethe signals are not strong enough to broadcast through the othercontainers. Embodiments of the present disclosure cure this deficiencyby matching a shipper's global supply chain data with the MMSI number,IMO number or name of the ship carrying the container from Port A toPort B.

Another embodiment allows shippers to track the fleet of ships carryingtheir goods at any one time. Such shippers may not be interested in theother ships being tracked, but the parameters could be set in anapplication of the disclosure to show shippers only vessels carryingtheir goods.

Embodiments of the disclosure can be used by parties such asmanufacturers and producers to track the global supply of any givencommodity. ‘Ibis helps them better manage their manufacturing processes,inventory, and supply chain. For example, ALCOA could track the globalflows of bauxite to ensure that they have sufficient inventory to keeptheir aluminum plants operating or, if there is a supply shock, toassess whether there arc available alternative supplies in proximity totheir aluminum plants affected by that shock.

Embodiments of the disclosure can be used by marine insurers to ensureinsured vessels or cargos are transiting in only approved geographies.Certain marine insurance policies, such as hull & machinery insurance,cargo insurance, and war risk insurance, have special provisions thatrequire additional premiums to be paid if a vessel enters a certaingeographical areas. For example, the Joint War Committee of Lloyd'sMarket Association and the International Underwriting Association ofLondon issues a list of risk areas on its website. Embodiments of thedisclosure can be used by banks and other lending institutions to trackvessels and cargos that they have financed.

Embodiments of the disclosure keep a record of past ship and cargo flowmovements, allowing for historical trend analyses of global ship andcargo flow movements. This is particularly valuable to commoditiestraders, freight traders, brokers, financial specialists, industryanalysts, economists, supply chain managers, insurers, internationalfinancial markets, governments, and other parties interested in worldtrade patterns, exports, imports, global economic trends, and commoditymovements.

A database of the present disclosure can sort data by geographic tradingareas, including North Asia, SE Asia, Europe, and North America (exactgeographical areas to be determined by market research). For example, Xships located in North Asia with Y cargo capacity and estimated transittimes to A, B, C ports.

Users can set up customized alerts for certain events, such as when avessel turns around, when a vessel makes significant deviation incurrent course, when a vessel makes significant speed change, when avessel arrives in port, or when a vessel departs a port.

Other customized alerts deal with aggregated cargo in vessels.Embodiments of the disclosure allow users to select a cargo type ofinterest, such as crude oil, and then create customized alerts for thatcargo type. Examples of customized alerts for crude oil include: when Xmillion barrels of crude oil enters the Mediterranean Sea, when Xmillion barrels of crude oil is within Y days sailing time for auser-defined port or set of ports, or when X million barrels of crudeoil is exported from a user-defined port or set of ports over Z timeperiod.

Embodiments of the disclosure allow users to create alerts of a supplydisruption or anomaly of a user-defined commodity or cargo type (e.g.,crude oil), or a user-defined set of commodities or cargo types. Suchalerts can be selected from a pre-defined list of shocks or created witha user-defined set of parameters. Examples of alerts for crude oilinclude: when X million barrels of crude oil has been diverted ordelayed by severe weather, when X million barrels of crude oil has beendiverted or delayed by piracy or a terrorist attack, when X millionbarrels of crude oil has been diverted or delayed by a navigationalhazard or obstruction in a key transit point (e.g., the Suez Canal),when X million barrels of crude oil has been diverted or delayed by amechanical problem at a crude loading-berth, or when X million barrelsof crude oil has been diverted or delayed by a mechanical problem at acrude offloading-berth.

Embodiments of the disclosure allow users to create alerts that flagoutliers from the historical data trends. Outlier alerts can be selectedfrom pre-defined settings or customized with user-defined settings.Outliers can provide market intelligence that could be used for atrading advantage. For example, an alert can be triggered the first timethat X million barrels of crude oil is imported into Y port during Zmonth. Outliers can also provide security intelligence that could beused for anti-piracy, anti-terrorism, drug interdiction, or othersecurity purposes. For example, an alert can be triggered when a shipwith an AIS signal is in a part of the ocean where it has not beenbefore or where few ships have previously ventured. Such data is anindicator of possible contraband shipment. Such outlier alerts willaccount for seasonal variations in shipping patterns.

Embodiments of the disclosure allow users to create customized alertsbased on predefined geographic areas, such as ocean basins, marketareas, transit points, and ports. These geographic areas havepre-defined parameters and users can select the geographic areas ofinterest.

Embodiments of the disclosure allow users to create alerts based oncustomized geographic areas. Users can draw a polygon on a map thatcovers a specific geographic area, and then create customized alertsrelated to the geographic area designated by that polygon.

Embodiments of the disclosure notify users of alerts by email, textmessage, fax, automated phone calls, mobile phone application, webinterface, data feed, or via a user-defined system.

Embodiments of the disclosure provide a sophisticated software filtercombining AIS satellite information with existing shipping databases toprovide comprehensive MDA. Governments can use embodiments of thepresent disclosure to achieve a critical security application calledmaritime domain awareness (MDA) Similar to tracking all aircraft in thesky by radar, MDA allows for tracking of all ships at sea to enforceapplicable laws and regulations, and prevent nefarious activity, such asillegal fishing in restricted zones, catching polluters dischargingprohibited substances (especially as a forensic tool), and catchingsmugglers of contraband, especially narcotics and human trafficking.Embodiments of the disclosure can be used to verify compliance withtreaty obligations, such as the UN Convention on the Law of the Sea,maritime boundary treaties between countries, and treaties governingfishing in restricted areas.

Embodiments of the present disclosure can be used as a forensic tool, toenforce environmental regulations, such as illegal dumping, shipemissions control areas, etc. For example, embodiments of the disclosurecould monitor and enforce ship emissions in the Sulfur Emission ControlAreas (SECAs), designated by the IMO, where merchant vessels arerequired to use low-sulfur fuel, or, given a spill, determining whichship may have been the polluter.

There are homeland security applications of an effective MDA picture aswell. Applications of the disclosure can be designed to receiveamplifying information from government security sources such asclassified intelligence and law enforcement data. These represent twoexamples of official, restricted data sets that could be added to theglobal strategic picture. In other words, this product could provideprivate sector platform on which the US Government, or other governmentsor authorities, add classified government intelligence and otherinformation to create a more robust MDA picture.

Embodiments of the disclosure can integrate analysis of high-resolutionsatellite images and infrared satellite collection with satellite-basedAIS data and land-based AIS data to provide a more complete strategicpicture for maritime domain awareness. In this regard, advances inArtificial Intelligence offer useful computer based tools for datamanipulation. Vessel, port, and cargo data from other sources can alsobe integrated into this maritime domain awareness picture, to provide aglobal strategic picture of vessel and cargo movements for securitypurposes.

Embodiments of the disclosure can be used to enforce the regulations ofthe IMO, the US Coast Guard, and other maritime enforcement agencies.For example, embodiments of the disclosure detect which vessels haveincorrect MMSI numbers or incorrect IMO numbers in their AIS systems.

Embodiments of the disclosure can be used as a recovery tool to increasethe marine transportation system's post-incident resiliency—after adisruption by terrorist attack, hurricane or other natural disaster, orhuman-related accident—by allowing officials to prioritize ship entry inthe queue of waiting or approaching ships. Priority can be given tocertain cargos, vessel types, or vessels with certain characteristics(e.g., shallow draft vessels that could avoid navigational hazardsrelated to an incident). For example, in the event that severe weatherdisrupted the Boston area's natural gas pipeline system, the US CoastGuard could use applications of the disclosure to give priority to awaiting LNG carrier to dock at the LNG offloading-berth in Everett and,thus, avoid a power outage at the power plant next to the terminal.

Embodiments of the disclosure can match available post-incident portcapacity with waiting or approaching vessels by comparing (i) port data,such as cargo facilities, storage capacity, and channel depth with (ii)vessel location data, and (iii) vessel data, such as cargo type, cargocapacity, and vessel draft.

Embodiments of the disclosure can improve post-incident intermodalefficiency by identifying which transportation modes pipelines, truckingrail, maritime, and air have available capacity and which transportationmodes suffer from temporary disruption. For example, after the 9/11attacks, with land-based transportation systems disrupted, ferries andmerchant vessels helped evacuate lower Manhattan.

Embodiments of the disclosure provide worldwide tracking of specificvessel(s) of interest, such as LNG carriers, vessel types carryinghazardous cargos, known USCG list of safety violators, suspect vesselsknown to be associated with nefarious activity, North Korean flaggedvessels, etc. Combinations of high interest vessels can be tracked andlive data streams of their location can be produced. Periodic watchlists can be generated in tabular form or a geospatial picture such asan overlay on Google Earth or Google Maps can be created according toillustrative embodiments.

Embodiments of the present disclosure can be used for distinguishingthreats from legitimate commerce more quickly thereby improving nationalsecurity resiliency. For example, deviations from normal cargo flows canalert intelligence officials to an elevated threat, allowing them tofocus limited resources on suspicious activities by distinguishing themfrom legitimate commerce.

Embodiments can be used to monitor what vessels and cargo flows arearriving/leaving particular ports or countries of interest, such asIran, North Korea, or known narcotics exporting locations, or formonitoring regulation of fishing fleets. For example, intelligenceagencies can monitor how much of a certain cargo, such as grain, a givencountry imports offering inferential information on food production andthe presence of famine.

Embodiments of the disclosure provide an additional safeguard to protectpotential victims of piracy in dangerous waters. For example,intelligence agencies and anti-piracy patrols could track vessel typesand cargo types in piracy risk areas to focus anti-piracy efforts onvessel types and cargo types that present an elevated risk of pirateattack (e.g., slow-moving laden oil tankers have a higher risk of beingattacked by pirates than a fast-moving container vessel).

Embodiments of the disclosure can assist in search and rescue operationswherein software can help identify vessels in distress and assist infinding nearby ships to render assistance. Such embodiments are similarto AMVER, except ubiquitous and comprehensive thereby serving as asearch and rescue tool to direct responding USCG assets and identifycommercial vessels which may render assistance to a nearby ship indistress.

Scientists, environmentalists, industry and living marine resourcemanagers can use the various embodiments of the disclosure to track andunderstand shipping's impact on the marine environment. For example, theIMO designates certain Sulfur Emission Control Areas (SECAs) wheremerchant vessels are required to use low-sulfur fuel.

In a future global cap and trade system and carbon market, ships willalso be required to comply with established emission standards.Embodiments of the present disclosure allow for the policing of vesselexhaust discharge, where regulations require ships to burn cleaner fuelswhen near shore.

Embodiments of the disclosure can integrate the tracking of seabornecommodities in transit with cargo information collected from othertransportation modes (such as pipelines, trucks, freight trains,helicopters and airplanes) to provide a global intermodal picture ofcommodity movements.

Embodiments of the disclosure integrate sea state into vessel speedcalculations. Sea state influences the speed at which vessels mayoperate. For example, in heavy seas, vessels operate at a slower thannormal speed. Integrating sea state into the disclosure provides a moreaccurate global picture of seaborne commodity movements for particularapplications.

Embodiments of the disclosure can dynamically generate “license plates”or “unique signature” of critical attributes required forclients/customers out of the varied data streams through intelligentmining and search techniques.

Examples of the present disclosure may include a first illustrativeembodiment which tracks the world's LNG carrier fleet and combines thatship location information with data on the LNG fleet from Lloyd'sRegister—Fairplay, Lloyd's MIU, Clarksons, and Q88. A secondillustrative embodiment of the disclosure may add the world's crudecarrier fleet—including Very Large Crude Carriers (VLCCs), Ultra LargeCrude Carriers (ULCCs), and Suezmax tankers—to the LNG fleet. A thirdillustrative embodiment of the disclosure may add other vesselcategories that carry only one cargo type, for example.

In an illustrative example of databases according to the disclosure,data from disparate sources is integrated by creating a database thatcombines (i) ship location data from Orbcomm's AIS data, COM DEV's AISdata, and data from terrestrial-based AIS networks with (ii) vessel datafrom Clarksons, Lloyd's Register—Fairplay, Lloyd's MIU, Q88.com, andBaltic99.com, loaded/empty status, likely cargo type (e.g., grade ofcrude oil), and likely amount of cargo on board derived from arules-based logic using last port of call and a list of dedicatedloading- and offloading-berths (e.g., for crude oil) or vessels engagedin lightering, and (iv) cargo, weather, market, and other data frombrokers, charterers, shipowners, cargo manifests, bills of lading, andmarket intelligence, for example.

The database is extensible to additional fleets and vessels, and to moredata sources in the future (e.g., adding Lloyd's MIU and Q88 vessel datato the Clarksons and Lloyd's Register—Fairplay vessel data), Thedatabase is designed to allow for a time history of the various datacombinations, providing the Z-axis in the data cube. The database issortable to determine the current location (latitude/longitude), currentcourse, and current speed of (i) the entire crude oil tanker fleet, (ii)only fully loaded crude oil tankers, (iii) only empty crude oil tankers,and partially loaded crude oil tankers (e.g., 60% loaded), for example.

The following exemplary list of crude oil grades and types illustratesthe complexity of crude oil as a commodity, and the value of adding thiscargo information into the global strategic picture of crude oilmovements: Abu Bukhoosh, Al Shaheen, Alaska North Slope, Alba, AlgerianCondensate, Amna, Anasuria, Arab Extra Light, Arab Heavy, Arab Light,Arab Medium, Arab Super Light, Ardjuna, Arun Condensate, Asgard, Attaka,Azadegan, Azeri Light, Bach Ho, Bachaquero, Balder, Basrah Light, BCF17, Belayim Blend, Belida, Benchamas, Beryl, Bintulu Condensate, BonnyLight, Bontang Condensate, Boscan, Bouri, Bow River, Brass River, Brega,Brent Blend, Brent Sweet, Brunei Light, Cabinda, Canadon Seco, CanoLimon, Captain, Ceiba, Cerro Negro, Champion, Cinta, Cold Lake, Cossack,Cusiana, Daqing, Djeno, Doba Blend, Draugen, Dubai, Dukhan, Dulang,Duni, Ekofisk, Es Sider, Escalante, Escravos, Fife, Flotta, Foinaven,Forcados, Foroozan Blend, Forties, Fulmar, Furrial, Galcota Mix,Gippsland, Girassol, Glitne, Gryphon, Gullfaks, Handil Mix, Hanze,Harding, Heidrun, Hibernia, Iran Heavy, Iran Light, isthmus, Jasmine,Jotun, Khafji, Kirkuk, Kittiwake, Kole, Kuito, Kutubu Blend, Kuwait,Labuan, Laminaria, Lavan Blend, Light Louisiana Sweet, Liuhua, LiverpoolBay, MacCulloch, Mandji, Maureen, Marib, Marlim, Mars Blend, Masila,Maya, Medanito, Minas, Miri, Mixed Blend Sweet, Murban, N'kossa, NangNuang, Nanhai Light, Napo, Ncmba, NFC II, Nile Blend, Njord, Nome, NWShelf Condensate, Olmeca, Oman, Oriente, Oseberg, Oso Condensate,Palanca Blend, Panyu, Pennington, Pierce, Plutonio, Poseidon Streams,Qatar Marine, Qua Iboe, Rabi, Rincon, Ross, Saharan Blend, Sakhalin II,Sarir, Schiehallion, Senipah, Scria Light Export, Shengli, SiberianLight, Ski, Sirri, Sirtica, Slcipncr Condensate, Snorre, Souedieh, SouthAme, Statfjord, Suez Blend, Syncrude Sweet Blend, Syrian Light, Tapis,Tempa Rossa, Tengiz, Terra Nova, Thamama Condensate, Tia Juana Heavy,Tia Juana Light, Triton, Troll, Turkmen Blend, Umm Shaif, Upper Zakum,Urals, Varg, Vasconia, Wafra, West Texas Intermediate, Widuri, WytchFarm, Xikomba, Yoho, Zafiro, Zakum, Zarzaitine, Zuata Sweet, Zueitina,etc.

In addition to reporting current course and speed, the average courseand average speed of each type of vessel (e.g., crude oil tankers) iscalculated over X number of hours, as well as the average speed of theentire fleet for each type of vessel (e.g., crude oil tankers) over Xnumber of hours. The average speed of the entire fleet for each vesseltype (e.g., crude oil tankers) is disaggregated into the average speedof subsets of that fleet, such as (i) fully loaded vessels, (ii) emptyvessels, and (iii) partially loaded vessels (e.g., 60% loaded). Theaverage speed of the entire fleet for each vessel type (e.g., crude oiltankers), and its subsets (e.g., loaded crude oil tankers) is alsodisaggregated geographically by port(s), country or countries, oceanbasin(s), port pairs, country pairs, ocean basin pairs, sea route(s),and key transit points. For example, the example embodiment cancalculate the average speed of loaded crude oil tankers that departedports in Saudi Arabia, the average speed of loaded crude oil tankers inthe Indian Ocean, or the average speed of loaded crude oil tankers thattransited the Suez Canal.

In an exemplary interface of the disclosure, a geospatial interface usesa drop down menu to sort the visual display for (i) the entire worldfleet, including options to display the world fleet for each type ofvessel (e.g., crude oil tankers), (ii) only loaded vessels, includingoptions to display only laden vessels for each type of vessel (e.g.,crude oil tankers), (iii) only empty vessels, including options todisplay only empty vessels for each type of vessel (e.g., crude oiltankers), or (iv) partially loaded vessels, including options to displayonly empty vessels for each type of vessel (e.g., crude oil tankers).

In an example of a geospatial interface according to the disclosure,“loaded” and “empty” merchant vessels are color-coded triangles thatpoint in the direction that the vessels are sailing (e.g., loadedvessels are green triangles, while empty vessels are white triangles).Different vessel types, such as crude oil tankers, can have differentcolors or symbols. it is to be appreciated that data presentation,including presentation by graphical user interface, is a rapidlydeveloping area. The foregoing example is presented as a non-limitingillustrative example, and new data aggregation and presentation toolsare being constantly made available.

In the example, clicking on a ship icon provides basic vesselinformation (ship name, cargo capacity, last port of call, averagecourse over last hour, average speed over last hour). Clicking on a porticon provides basic port information (e.g., crude oil exports/importsover X time period, which would be calculated by the software by addingcargo capacity of crude oil tankers calling at crude oil loading- andoffloading-berths over X time period). Clicking on a country providesbasic import/export information (e.g., crude oil exports/imports over Xtime period). This would aggregate similar data from the country's crudeoil export/import facilities.

The “X days from Y port information” can be geographically displayedwith lines emanating from the ships on the screen to potential ports ofcall with estimated time of arrival calculated from average speed overthe last Z hours, for example. For certain types of vessels, such ascrude oil tankers, vessel type can be combined with port type to limitthe number of potential ports of call. For example, a crude oil tankerin the North Atlantic would only have lines connecting to crude oilimport facilities in North America and Europe.

The example interface has the capacity to block out certain sensitiveareas for safety/security purposes, such as piracy hot spots nearSomalia.

In another example interface, the entire world fleet for a commodity isrepresented in terms of volume of global supply and expected time toreach port. For example, from port of Houston, the short term supplypicture for crude oil would be displayed as:

35 M barrels/1 day, 13 hours, 32 minutes

125 M barrels/3 days, 5 hours, 18 minutes

64 M barrels/8 days, 8 hours, 52 minutes

The time to destination would be updated based on recalculations of theroute, average speed, and imputed destination of the ships as they comein.

Another functionality generates alerts when there is any substantialchange in the short-term projections of supply. The user has the abilityto define a threshold of change in volume of supply, expected time ofarrival, or port of arrival such that the application generates an alertany time the forecast for the designated commodity changed above thethreshold value. For example, the threshold value could be defined as achange in expected arrival time by more than 1 day. In the exampleabove, if a hurricane in the Atlantic caused ships carrying the 125 Mbarrels of crude oil to go to port.

Example output might be, in text form:

35 M barrels/1 day, 13 hours, 32 minutes

**supply shock alert**

→125 M bbl/3 days, 5 hours, 18 minutes→now 125 M bbl/6 days, 18 hours,18 minutes→+3 days, 13 hours64 M bbl/8 days, 8 hours, 52 minutes

The illustrative embodiment of the disclosure would also generate avisual representation of the supply shock in graphical icons in the userinterface.

An illustrative embodiment of spreadsheet functionality associated withthe disclosure provides an Excel spreadsheet in which a bottom frame ofthe exemplary web-based user-interface includes several lists ofboxes/categories to check (these lists of boxes/categories are outlinedbelow). Note that spreadsheet is to be broadly construed to include anydata aggregation graphic, including paper graphs and charts as well as“on-screen” type displays. Each user can check the desiredboxes/categories, and then click a button to create an Excel spreadsheetpresenting the results of their inquiry. The user can then manipulatethe data however they like for the fields selected to generate thespreadsheet.

An example spreadsheet according to an illustrative embodiment is“fresh” at the time it was generated. The user can generate updatedspreadsheets over time as new AIS data is gathered. Spreadsheets canalso be generated manually according to the exemplary embodiment of thedisclosure. In an automated embodiment, a user may create a customizedsearch that delivers a particular Excel spreadsheet by email hourly,daily, or weekly.

An example spreadsheet can also provide the time history of ship andcargo flow movements, allowing users to conduct historical trendanalysis of global ship and cargo flow movements.

An exemplary website interface combines a Google Earth or Google Mapsdisplay with an Excel spreadsheet download interface. The Google Earthinterface is illustratively provided on a top frame. The Excelspreadsheet download interface displays several groups ofboxes/categories to check which are downloadable in an Excelspreadsheet.

Core data appears at the bottom of Google Earth or Google Maps display.These data are sortable by vessel or cargo type. For example, for crudeoil, the data appearing at the bottom of the Google Earth or Google Mapsdisplay may include:

Average speed of loaded crude oil tanker fleet

Total amount of crude oil cargo in transit at sea

Total amount of crude oil tanker tonnage in ballast (empty crude oiltankers)

Total amount of crude oil cargo exported in last 24 hours

Total amount of crude oil cargo imported in last 24 hours

Using a drop-down menu, users can display these data fields for othervessel types, such as LNG carriers, LPG carriers, product tankers,chemical tankers, bulk tankers, iron-ore carriers, bauxite carriers,grain carriers, livestock carriers, pure car carriers, lumber carriers,cruise ships, passenger vessels, etc.

Pop-up boxes appear on the geospatial interface when users click on acountry, port, vessel, or ocean basin. These data are sortable by vesselor cargo type. For example, for crude oil, pop-up boxes will providesuch data as:

Country: crude oil imports/exports—24 hours, monthly, quarterly,annually Export facility: crude oil exports—24 hours, monthly,quarterly, annually

Import facility: crude oil imports—24 hours, monthly, quarterly,annually

Vessel: Name, cargo capacity, latitude/longitude, course, speed, lastport of call

Ocean basin: Average speed of loaded crude oil fleet, total amount ofcrude oil in transit, total amount of crude oil tanker tonnage inballast

Market area:

Asian market area—NE Asia and SE Asia

European market area

North American market area

Using a drop-down menu, users can display pop-up boxes for other vesseltypes.

Users can download vessel data in Excel spreadsheets. The user interfacewill allow users to check boxes of the data fields that they want, andthen press a button to download those data in an Excel spreadsheet.Using a drop-down menu, users can sort the vessel data to download byvessel type. For example, users can download such data as the followingfor crude oil tankers:

Crude oil tankers (vessel name, IMO number, MMSI number)

Cargo capacity

Loaded/empty/partially loaded status

Vessel tonnage

Current location (latitude/longitude)

Current course

Current speed

Last port of call

Destination port(s) (% of historical track record or imputed fromrules-based logic)

Average course over 24 hours, 72 hours, 7 days

Average speed over 24 hours, 72 hours, 7 days, 15 days, 30 days, 90days, year Average speed of loaded crude oil tanker fleet over 24 hours,72 hours, 7 days, 15 days, 30 days, 90 days, year

Average speed of empty crude oil tanker fleet over 24 hours, 72 hours, 7days, 15 days, 30 days, 90 days, year

Average speed of partially loaded crude oil tanker fleet over 24 hours,72 hours, 7 days, 15 days, 30 days, 90 days, year

Total amount of crude oil cargo in transit at sea

Total amount of crude oil tanker tonnage in ballast

Total amount of crude oil cargo exported in last 24 hours

Total amount of crude oil cargo imported in last 24 hours

The Excel spreadsheet can contain data fields for all individual crudeoil tankers. It can also aggregate the data for the entire crude oiltanker fleet and subsets of the crude oil tanker fleet, such as loadedcrude oil tankers, empty crude oil tankers, and partially loaded crudeoil tankers. It can also aggregate or disaggregate geographically byport(s), country or countries, ocean basin(s), port pairs, countrypairs, ocean basin pairs, sea route(s), and key transit points. Forsecurity purposes, the Excel spreadsheet can also remove vessel-specificinformation to show only the cargo movements associated with individualvessels.

Users can download cargo data in conventional spreadsheets (e.g.,Excel). The user interface will allow users to check boxes of the datafields that they want, and then press a button to download those data inan Excel spreadsheet. Using a drop-down menu, users can sort the cargodata to download by cargo type, such as crude oil, or by vessel type,such as crude oil tankers. For example, users can download such data asthe following for crude oil:

Crude oil flows

Flows of different crude oil grades

Amount of crude oil on board each crude oil tanker

Port of origin

Destination port(s) (% of historical track record or imputed fromrules-based logic)

Historical record of latitude/longitude of individual crude oilmovements

Geographical location of individual crude oil movements (by ocean basin,sea route, key transit points, etc.)

Geographical location of aggregated crude oil movements (by ocean basin,sea route, key transit points, etc.)

Average course over 24 hours, 72 hours, 7 days

Average speed over 24 hours, 72 hours, 7 days, 15 days, 30 days, 90days, year

Total amount of crude oil cargo in transit at sea

Total amount of crude oil cargo exported in last 24 hours

Total amount of crude oil cargo imported in last 24 hours

The Excel spreadsheet can aggregate or disaggregate geographically byport(s), country or countries, ocean basin(s), port pairs, countrypairs, ocean basin pairs, sea route(s), and key transit points.

Users can download port data in conventional spreadsheets (e.g., Excel).The user interface will allow users to check boxes of the data fieldsthat they want, and then press a button to download those data in anExcel spreadsheet. Using a drop-down menu, users can sort the port datato download by port type, such as crude oil import facility, crude oilexport facility, LNG import facility, LNG export facility, refinedpetroleum product port, chemical port, bulk port, container port, lumberport, automobile (pure car carrier) port, passenger terminal, etc. Forexample, users can download such data as the following for crude oilport facilities:

Crude oil ports (port name) Export/import facility

Geolocation (latitude/longitude) Loading/unloading capacity

Storage capacity

Crude oil exported/imported over last 24 hours, 72 hours, 7 days, 15days, 30 days, 90 days, year

These port data can also be aggregated and organized on the countrylevel, the regional level, or according to a user-defined set of ports.

Users can download data organized by port pairs (e.g., vessel/cargoflows from Ras Tanura, Saudi Arabia to Houston, Tex.) in conventionalspreadsheets (e.g., Excel). The user interface allows users to checkboxes of the port pairs that they want, and then press a button todownload those data in an Excel spreadsheet. Using a drop-down menu,users can sort port pair data to download by port type, such as crudeoil import facility. For example, users can download such data as thefollowing for crude oil port pairs:

Crude oil loading-berths first column (port name)

Crude oil offloading-berths first row (port name)

The amount of crude oil cargo moving from export to import facilities.

The crude oil tankers moving between export and import facilities.

The aggregated crude oil tanker tonnage moving between export and importfacilities.

These port pair data could also be aggregated and organized on thecountry level, which would provide users with the amount of cargo orvessel tonnage flow between two or more countries, the regional level,or between a user-defined set of ports.

Users can download data organized by ocean basin, ocean basin pairs, searoute, and key transit point in conventional spreadsheets (e.g., Excel).The user interface will allow users to check boxes of the data fieldsthat they want, and then press a button to download those data in anExcel spreadsheet. Using a drop-down menu, users can sort the data todownload by categories. The drop-down menu will also allow users to sortthe data by cargo type, such as crude oil, or by vessel type, such ascrude oil tankers. For example, users can download such data as thefollowing for categories of interest:

Individual vessel names

Individual vessel IMO numbers or MMSI numbers Cargo capacity, by vesseland aggregated for fleet Laden/in ballast/partially full status

Amount of crude oil on board each vessel Tonnage, by vessel andaggregated for fleet

Current location (latitude/longitude)

Current course

Current speed

Last port of call

Destination port(s) (% of historical track record or imputed fromrules-based logic)

Average course over 24 hours, 72 hours, 7 days

Average speed over 24 hours, 72 hours, 7 days, 15 days, 30 days, 90days, year

Average speed of loaded crude oil tanker fleet over 24 hours, 72 hours,7 days, 15 days, 30 days, 90 days, year

Average speed of empty crude oil tanker fleet over 24 hours, 72 hours, 7days, 15 days, 30 days, 90 days, year

Crude oil port facilities

Users can also create customized alerts for individual vessels, types ofvessels, types of cargo, weather, port congestion, market data, economicdata, export data, import data, world trade patterns, and other trendsor events. The following list provides some examples:

Supply shock in a certain commodity

Deviation in expected arrival time of certain commodity flows exceeds auser-defined threshold

Cargo amount of a certain commodity in a user-defined geographical areaexceeds a user-defined threshold

Cargo amount of a certain commodity in an ocean basin exceeds auser-defined threshold Cargo amount of a certain commodity on a searoute exceeds a user-defined threshold Cargo amount of a certaincommodity passing through a key transit point exceeds a user-definedthreshold

Deviation in expected arrival time of a vessel exceeds a user-definedthreshold

Vessel turns around

Vessel makes significant deviation in current course

Vessel makes significant speed change

Vessel anchors in a harbor to engage in floating storage

Vessel engaged in floating storage starts moving to market

Port arrival by certain type of vessel

Port departure by certain type of vessel

World trade increases or falls by X % over a user-defined time period

Trade from a user-defined local, state, national, regional, orinternational geographic area increases or falls by X % over auser-defined time period

Exports from a user-defined local, state, national, regional, orinternational geographic area increases or falls by X % over auser-defined time period

Imports from a user-defined local, state, national, regional, orinternational geographic area increases or falls by X % over auser-defined time period

FIG. 4 depicts a method for generating trade and shipping activityreports (400). Data is received providing vessel locations of a globalfleet of vessels (402). The global fleet data may be provided bysatellite 104, ground based receiver 103, or transmitted by the globalfleet, or any other combination or multiplicity of these sources.Generally, a data center 106 may receive the data and perform theremaining operations.

The received vessel location data is then used to infer a vessel's cargocontents and load (e.g., loaded or empty) status by combining thelocation data with historical vessel location data, vesselcharacteristics, port characteristics, and commodity flow patterns(404). As discussed above, the vessel's cargo contents, load status, andcommodity flow patterns can be discerned in multiple ways, including,without limitation, using rules-based logic or Bayesian logic, neuralnetworks, learning algorithms, other mathematical methods, directinquiry to owners, shippers or port personnel or by historic data oradditional contextual or inferential data. Cargo contents, load status,commodity flow patterns, historical vessel location data, portcharacteristics, vessel characteristics can also be retrieved fromancillary databases 109 or vessel databases 110.

The inferred cargo and load data may then be combined with the receivedvessel location data, historical vessel location data, and physicalcharacteristics of the vessel and port physical characteristics to yieldcombined data (406). The combined data is then used to generate a reportdetailing either maritime trade or global fleet shipping activity (408).The generated report may be received by a server such as data center 112to be provided to users upon request or may be generated for, anddelivered to, a single user. A global strategic picture (GSP) processor308 may perform the combining operation 406 and deliver the data outputto a presentation processor 312 for report generation operation 408.

In some embodiments, method 400 may be performed periodically andthereby generate periodic logs. These periodic logs may be combined withuser defined data in order to generate a report for a specific subset ofvessels of the global fleet. In various other embodiments, thisgenerated user defined report may further be used over a user specifiedperiod of time to generate trend data for a subset of the fleet. Theuser specified trend may be output to user applications such ascommodity view applications 313, government and security applications314, or fleet management applications 315.

In some embodiments, the inferred cargo and load status of operation 404can be provided independently and along with the generated report ofoperation 408. The cargo data may be provided as a quantity of cargo andthe report may be provided as quantified data.

FIG. 5 depicts a method for determining a course or identification of avessel from incomplete or inaccurate position or identificationinformation (500). Incomplete or inaccurate vessel position or vesselidentification information is received (502). The incomplete orinaccurate vessel position information can be received from an automaticship identification system (AIS), an image of the vessel, radar data, ora combination of AIS and image data. The incomplete or inaccurate vesselidentification information may include any or a combination of a vesseltype, name, identification number, status, size, or capacity.

Port information describing a port is also received (504). The portinformation can include any or a combination of a port operationalstatus, position, capacity, size, draft restriction, handled cargos, andberth information. Berth information can include any or a combination oftotal number and location of berths, and cargos handled by the berths.Any physical characteristic of a port can be port information.

The received vessel position and/or identification information may thenbe combined with the received information on vessel type, name,identification number, status, size, or capacity in order to infer acourse or identification of the vessel (506). In some embodiments,weather information from, for example, a weather database may also becombined with the vessel position and/or identification information toinfer a course or direction of the vessel.

In some embodiments, the vessel position may be tracked over a period oftime to provide historical vessel position data. In some embodiments,port information can be tracked over a period of time as well or insteadin order to provide historical port information. In other embodiments,historical vessel information and historical vessel data may be used toinfer load and cargo information as well as an origin and destination ofthe vessel. By doing this to multiple vessels, vessel quantity andaggregated cargo can be determined as well.

In some embodiments, the method 500 may be performed on a plurality ofvessels to quantify information on a plurality of vessels or a fleet.Further, the quantified information on the fleet may be combined withknown vessel patterns to generate a quantification of the shippingactivity of the fleet.

In some embodiments, vessel position, speed, course, fleet averagespeed, port of origin, destination port, or time at port may be combinedwith known vessel patterns to generate a quantification of shippingactivity of a fleet of vessels. Fleet shipping activity can include,without limitation, ships entering and/or leaving geographical areassuch as those associated with a port or country, moving along searoutes, and/or idling.

A general example system shall be disclosed in FIG. 6 which can providesome basic hardware components making up a server, node or othercomputer system. FIG. 6 illustrates a computing system architecture 600wherein the components of the system are in electrical communicationwith each other using a connector 605. Exemplary system 600 includes aprocessing unit (CPU or processor) 610 and a system connector 605 thatcouples various system components including the system memory 615, suchas read only memory (ROM) 620 and random access memory (RAM) 625, to theprocessor 610. The system 600 can include a cache of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 610. The system 600 can copy data from the memory 615and/or the storage device 630 to the cache 612 for quick access by theprocessor 610. In this way, the cache can provide a performance boostthat avoids processor 610 delays while waiting for data. These and othermodules/services can control or be configured to control the processor610 to perform various actions. Other system memory 615 may be availablefor use as well. The memory 615 can include multiple different types ofmemory with different performance characteristics. The processor 610 caninclude any general purpose processor and a hardware module or softwaremodule/service configured to control the processor 610 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 610 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus (connector), memory controller, cache, etc. Amulti-core processor may be symmetric or asymmetric.

To enable user interaction with the computing device 600, an inputdevice 645 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 635 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 600. The communications interface640 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 630 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 625, read only memory (ROM) 620, andhybrids thereof.

The storage device 630 can include software services for controlling theprocessor 610. Other hardware or software modules/services arecontemplated. The storage device 630 can be connected to the systemconnector 605. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 610, connector 605, display 635, andso forth, to carry out the function. A computer-readable medium orcomputer-readable storage device are non-transitory devices and do notencompass the air interface or a signal per se.

Another aspect of this disclosure relates to vessel routing management.This can refer to any type of vessels including boats, trucks, trains,helicopters, drones, airplanes, and so forth. In one aspect, a method700, disclosed in FIG. 7, includes receiving digital vessel data for aglobal fleet of vessels, the digital vessel data being retrieved atleast in part from one or more of AIS data, image data and/or radardata, received from one or more sources such as satellite, a groundbased receiver or data transmitted by respective vessels of the globalfleet of vessels (or other source) (702), combining, via a processor,one or more of the digital vessel data, historical vessel location data,vessel location data, vessel physical characteristics data, portphysical characteristics data associated with a port and known patternsof maritime trade flows, to yield first combined data (704) andinferring, based on the first combined data, a loaded/empty status of atleast one of a vessel or a type of cargo in the vessel (706). Theinferring can also be more granular in that it can involve inferring theparticular level of cargo within the vessel. For example, the step caninfer that the vessel is 60% loaded. Thus, the loaded/empty status doesnot necessarily mean inferring that the vessel is fully loaded or fullyempty. The vessel data can also include reconstructed data associatedwith any message delivered from a vessel.

The method can further include combining, via the processor, one or moreof the digital vessel data, the vessel location data, the historicalvessel location data, the vessel physical characteristics data, the portphysical characteristics data, the type of cargo and the loaded/emptystatus of the vessel to yield second combined data (708), receiving dataregarding one or more of supply, demand, and amount of available cargoto yield third combined data (710), generating, based on one or more ofthe first combined data, the second combined data and the third combineddata, information relating to a supply of vessels available to load at aspecified port and/or deliver a cargo to a specified port, in each casewithin a specified period of time (712) and generating suggestions forone or more vessels regarding future routes based on one or more of thefirst combined data, the second combined data and the third combineddata (714).

The method can further include inferring the loaded/empty status of aplurality of vessels. The digital vessel data can include anycombination of the data such as two or more of AIS data, image data,radar data, and/or reconstructed message data, from any source such assatellite, ground-based receiver, or data transmitted by the globalfleet of vessels. Any combination of these various data types can beused for inferring the status of a vessel or vessels. It is also notedthat where port characteristics might be used, the vessel or vessels inthat scenario would likely be boats or other water bound vessels.However, where such port information is not part of the process, anykind of vessel is disclosed herein could be the subject of a particularclaim.

The first combined data can include any combination of the data such asa combination of two or more of the digital vessel data, the historicalvessel location data, the vessel location data, the vessel physicalcharacteristics data, the port physical characteristics data and theknown patterns of maritime trade flows.

The port physical characteristics data, when part of the analysis, caninclude one or more of an operational status of the port, a position ofthe port, a capacity of the port, a size of the port, a number of berthswithin the port, a location of the berths within the port, draftrestrictions at the port, cargo handled by the port, cargo handled atrespective berths within the port, and/or any other physicalcharacteristics of the port such as machinery available for loadingcommodities.

The radar data can include a radar image of the vessel. The radar datacan come from other vessels such as radar data from other boats orground-based radar detection systems, which can identify trucks, trains,drones, helicopters, airplanes, or any other vessel.

In another aspect, the system can include a variety of differentcomponents that provide a nonconventional combination of components forperforming the steps disclosed herein. For example, the system 802,shown in the configuration 800 of FIG. 8, can include a first datareceiving component 810 configured to receive digital vessel data 804,806, 808, for a global fleet of vessels, the digital vessel data beingat least one or more of AIS data, image data and/or radar data,retrieved from one or more of a ground based receiver, a satellite, ordata transmitted by respective vessels of the global fleet of vessels, afirst data combiner 812 configured to combine, via a processor, one ormore of the digital vessel data, historical vessel location data, vessellocation data, vessel physical characteristics data, port physicalcharacteristics data associated with a port and known patterns ofmaritime trade flows, to yield first combined data and an inferringcomponent 814 configured to infer, based on the first combined data, aloaded/empty status of at least one of a vessel or a type of cargo inthe vessel.

The system 802 can also include a second data combiner 816 configured tocombine, via the processor, one or more of the digital vessel data, thevessel location data, the historical vessel location data, the vesselphysical characteristics data, the port physical characteristics data,the type of cargo and the loaded/empty status of the vessel to yieldsecond combined data, a second data receiving component 818 configuredto receive data 804, 806, 808 regarding one or more of supply, demand,and amount of available cargo to yield third combined data and a firstgenerating component 820 configured to generate, based on one or more ofthe first combined data, the second combined data and the third combineddata, information relating to a supply of vessels available to load at aspecified port and/or deliver a cargo to a specified port, in each casewithin a specified period of time. The system 802 can also include asecond generating component 822 configured to generate suggestions forone or more vessels regarding future routes based on one or more of thefirst combined data, the second combined data and the third combineddata. The data sources 804, 806, 808 can represent any data sourcedescribed herein.

The concepts and applications disclosed herein can include determining avessel position using image data. Photographs, surveillance images,satellite images, radar images from a ground source, other vessels, or asatellite, can be used individually or in any combination with otherdata to determine a location of a vessel such as a truck, train,helicopter, drone, or boat as well as a stock level, inventory level,loaded level or status of the vessel, and so forth.

Additionally, using the vessel position, cargo data, and, whereapplicable, port data can be utilized in the efficient management ofshipping vessels. For example, the algorithms and systems disclosedherein can address the long-standing problem of how to optimize a fleetof vessels given supply, demand, location of the vessels, amount ofavailable product, and so forth. An example will illustrate the point.Assume that a vessel owner, in this example, the vessel is a ship, hasan offer to ship goods across the sea from this place to that place froma first entity for $12,000 a day. Assume the vessel owner has a secondoffer from a second entity to ship goods for $15,000 a day. While on thesurface it may be obvious to take the higher offer, using the principlesdisclosed herein, the system can optimize or improve the use of vesselused utilization. While a vessel owner will make more money when theyreceive a higher compensation for moving cargo, freight rates arevolatile and using the vessel position information together withinformation about vessels, ports, stock levels, and so forth, the systemcan determine an improved cargo and route for a vessel. For example, thesystem can provide information to the vessel owner that if they take the$12,000 per day contract, they will be able to spend more days in route,and at their destination port can pick up cargo for a return trip at$20,000 per day rather than returning empty if they take the $15,000 perday contract. This optimization can be pursued on a voyage by voyagebasis or across a portfolio of vessels or a fleet of vessels. Byutilizing the system disclosed herein, the evaluation can include thenext potential leg of shipping and all of the associated types of cargo,cost, distance, destinations, and so forth. The system can also evaluatethe following leg after that and beyond. In other words, therecommendation or suggestion to take the $12,000 per day cargo could bebased on the timing and positioning of one or more additional potentialshipments which can increase the profits of the shipping entity and theefficiency of the cargo distribution on a more global basis. In thismanner, the system can optimize the value of a fleet in terms of itsoverall efficiency and profitability. By providing a broader overview ofcargo movements, costs, and so forth, the system can also forecastfreight rates by using vessel location information, information aboutthe vessels and ports, and any data or communication data disclosedherein to forecast freight rates and to use the forecasted freight ratesto determine optimal vessel utilization, routes, timing, scheduling, andso forth.

In one example, the system may know that three vessels that can shipcars are all headed to a similar port in Japan. Knowing this data canprovide an opportunity for the system to forecast the rate for shippinga group of vehicles from Japan to America given the potential forcompetition. Any of the data or any combination of data disclosed hereincan be utilized to determine the market forces that might be at playwith respect to supply and demand on a granular basis such that moreaccurate forecasts of shipping costs, the availability of vessels,amounts of inventory, and so forth which can all go into play withrespect to a particular negotiated cost for shipping cargo, on aparticular vessel to a particular location.

For rail freight, the disclosure uses information about a type offreight car, such as a hopper or a container, the location of the trainon a railway, information about the shipper and destination,characteristics of train stations and loading stations such as the typeof cargo that is or can be loaded at a particular location (such is thedifference between coal and cars), the types of factories and warehousesthat are near a particular loading location, information about railwaynetworks and the trade of real cargo, the volume of real activity, andso forth. This is particularly of interest with respect to bulkcommodities. Accordingly, all of the concepts disclosed herein withrespect to boats, ports, and so forth can also be applicable to railfreight in principle. Any one or more of these data points can becombined to analyze the data and infer a loaded or empty status of thetrain, the type of cargo, supply, demand, and so forth.

With respect to truck freight, the information that can be used caninclude information about the types of trucks, the location and movementof trucks, ports or loading dock characteristics, and manufacturinglocations as well as other manufacturing facilities and retaildistribution centers and their characteristics, including physicalcharacteristics such as the number of loading docks, the height ofloading docks, the size of warehouse or distribution center, and soforth. The system can enable the planning for the most efficient use oftruck fleets with the goal of optimal use or preferred use of trucks viathe reduction of wait time and empty miles. The system can enable thematching of cargo with available trucks and provide information abouttracking networks or utilize information about trucking networks to makerecommendations or decisions as a result of the algorithms. Any of theprinciples disclosed herein with respect to boats in the analysis withrespect to inferring cargo routing and other data with respect to globalcargo distribution via boat can be also applicable to truck freight aswell.

In another aspect, the disclosure can also be viewed from the standpointof a superset network which receives and processes information about atrade network that includes maritime, rail and truck components on anintegrated flow of product or commodities. Thus, while the processes canbe implemented on an individual vessel type basis, the overall processcan also include application to cargo as it is transferred acrossdifferent types of vessels from its origination or generation to itsfinal destination at a retail store, a home, a warehouse, or whateverfinal destination.

A superset network can include a digital map of current and historicaltrade network activity and monitoring of the trade activity on asuperset basis. The monitoring can relate to the supply and demand oftrade. Machine to Machine (M2M) communication can also be tapped andprovided to the system for analysis. For example, such communications beused for the tracking of ships, trains, helicopters, trucks,construction equipment, mining equipment, farming combines, tractors,cell phones, EZ Pass data, radar data, AIS data, and any other similartype of data can be used, to generate a digital footprint created frominformation obtained from such digital sensors between machines. Any oneor more of these types of data can be provided to the system in order tocreate the digital footprint identification of a vessel, its movement,its status, and so forth can be utilized ultimately infer information.

For example, with farm equipment, the system could retrieve radar data,image data, AIS data (for land-based equipment), any visual data,infrared signature data, and/or any other kind of data to determinepatterns of farm combines or any other farm equipment to determinetiming of planting, harvesting and delivery to the marketplace. Volumeand expected yield can be determined based on a speed of farm equipmentas they move through a field or a timing of processing of crops relativeto weather patterns and weather conditions. Like ships, farm equipmentcan be equipped with message delivery systems which can provide themachine to machine communication and enable a tracking of their motionfor activity. This data can then be analyzed by the system in order topredict commodity movement, volume of crop harvests, type of crop,through the entire infrastructure or to infer certain characteristicsassociated with that particular commodity. The data cannot only includeprocessing of existing crop, but also track the seating operation aswell which can also be used to predict a harvesting time and/or volume.A crop type could also be inferred as well. Based on any of this data.In another aspect, the system can provide a mechanism for individualfarmers to submit their own information to a user interface to report onwhat crops they are planting or harvesting. Thus, the system disclosedherein can provide an overall view of commodities or any product fromthe very beginning of the creation of the product or commodity throughthe process of delivering the product or commodity to its finaldestination through all sorts of delivery mechanisms.

In one aspect, this disclosure involves the use of temporal informationto connect such messages, or such communications. For example, theirtips different types of messages broadcast by vessels in separatetransmissions. These can be AIS messages or other types of messages suchas position information messages. These messages can be transmittedperiodically and completely independent of each other. In other words,they do not necessarily get transmitted together. These messages can bepicked up by satellite, other vessels, boats, or any antenna that canreceive the message. It is often the case that one wants to associateone type of AIS message to another type and ultimately to a vessel. Thisapproach can be challenging as the AIS messages are sometimes mangled,corrupted or intentionally changed. Each message should include an MMSI(maritime mobile service identity) which is an identification number forthe transmitting vessel. Where the MMSI number is mangled or notreadable, users will typically just discard the message as not usefuland wait for the next one.

Part of this disclosure is to evaluate temporally these messages and todetermine a temporal correlation between the time series of differentmessages and the use of temporal information to associate a method ofmessage with each other and with vessels. For example, the system coulduse any one or more of port data, radar data, image data, AIS data, anymessage data from any source such as a satellite and so forth which canbe used to infer or specifically identify a specific location of avessel at a particular time. Where the system might receive a mangled,corrupted or intentionally changed message at a particular time, thesystem can correlate the time of that message or messages and be able toinfer or fill in some of the missing information, such as a transmittingvessel, based on the other data known about vessels which relate to orappear to be at the location of the transmitting vessel. In order toidentify the temporal data associate with the message, the system needsto evaluate the time that the system received the message as well as thepath traveled by that message. For example, if the message was receivedby a satellite and then forwarded to the system for evaluation, then ananalysis can be made to predict or infer when the message was actuallytransmitted and from what vessel. The inference of the transmission timefrom the vessel of the message can be correlated with vessel positionand timing information to associate a particular message with the vesseland/or with other potential messages from the vessel. For example, if amangled message can be correlated with other messages, then data withinthat message that was not retrieved can be inferred with confidence.

A method with respect to handling M2M messages his disclosed in FIG. 9.The method includes receiving a message (902), determining a time atwhich the message was transmitted (904), and mapping the message via atemporal correlation to determine an association of the message withother messages (906). For example, assume a vessel is periodicallytransmitting different messages about its location and itsidentification. Some messages are received via a land-based antenna andothers are received via satellite. Where some of these messages might bemangled and essentially unreadable on an individual basis, the methoddisclosed herein can receive the various messages, evaluate thetransmission path, and any other data associated with the message andmap the message into a temporal correlation with other messages. In thismanner, where the system might receive a clean message which properlyidentifies a vessel and its location and then a series of mangledmessages which are unreadable, using a temporal correlation betweenthese messages, the system can either positively identify or infer witha certain threshold of confidence that other messages were transmittedby the same vessel from a particular location or along a predicted path.This analysis enables the replacement or filling in of gaps of data frommessages that might otherwise be discarded and useless. Depending on theconfidence level of the messages, the data associated with the messagescan be forwarded to other analyses or components which can include thedata in an evaluation of shipping operations, and any other conclusionsor infer data disclosed herein.

The analysis of course could also involve simply a single message. Thesystem can receive a single message, identify or predict the temporalcomponent of the message which identifies when it was likelytransmitted. Given the location of the receiving antenna, whether asatellite or an earthbound station, the system could also correlate thetiming and general location of the transmitting vessel with other datathat identifies vessels and their likely locations to fill in themissing gaps associated with that message. The message may have traveledover several different networks and through several different nodes toarrive at the system for evaluation. Thus, the temporal analysis caninclude an indication of travel times across the various networkcomponents, delay factors, buffering factors, weather factors, and soforth, to ultimately predict a timing of when the message wastransmitted from the transmitting vessel. Thus, if the MMSI data is notreadable and the message, or only partially readable, and the otheranalysis identifies that a particular vessel is likely to have been inthe vicinity of the vessel that transmitted the message, the system canidentify a correlation between that particular vessel and the MMSI datato arrive at a positive identification of the vessel at a certain levelof certainty.

Therefore, the above analysis and processes enable the system to handlemanged, incomplete or inaccurate data in a novel way to create new dataor to infer or fill in the gaps of the missing data associated withthese types of messages. Rather than merely discarding such a message asuseless, the system can perform a temporal analysis, predict when themessage was sent, utilize all the other data disclosed herein, or anycomponent of the other data, and turn a previously mangled and uselessmessage into a potentially valuable message. The final result may be acompletely deciphered message or may be only partially decipheredmessage, but with sufficient information to be valuable, such as thevessel name or vessel position. Different portions of the message may bedeciphered or determine the different thresholds of confidence and theparticular structure and confidence levels of the resulting message canthen be provided to other components to include in a larger analysis ofshipping operations.

The disclosure now turns to various examples of the system and methodsdisclosed herein. The first relates a vessel, truck, train, drone, andairplane routing concept. This is shown in FIG. 10, a method includesreceiving digital vehicle location data for one or more vehicles, theelectronic real-time vehicle location data being at least one or more ofimage data, infrared data and/or radar data (or other data) retrievedfrom one or more of a ground based receiver or data transmitted by thevehicle (1002), inferring a loaded/empty status of the vehicle and/or acargo type of cargo in a vehicle by combining, via a processor, one ormore of the digital vehicle location data, historical vehicle locationdata, vehicle physical characteristics data and known patterns of cargoflows (1004), combining, via a processor, one or more of the digitalvehicle location data, the historical vehicle location data, the vehiclephysical characteristics data, the cargo type and the loaded/emptystatus of the vehicle to yield first combined data (1006), receivingdata regarding one or more of supply, demand, and amount of availablecargo to yield second combined data (1008) and providing informationrelating to the supply of vehicles available to load at a specified portor manufacturing location and/or deliver a cargo to a specified port ormanufacturing location, in each case within a specified period of time(1010), and providing suggestions for one or more vehicles regardingfuture routes based on the first combined data and the second combineddata (1012).

Another example method relates to vessels and rail applications. Anexample method is shown in FIG. 11. A method includes receiving andcombining, at a server, vessel position information for a vessel, vesselidentification and characteristic information for the vessel and portdata associated with a port, to yield first combined data (1102). Thevessel position information for the vessel is determined from at leastone of an automatic identification system (AIS) message (or any othermessage) from the vessel and an image of the vessel, which can be apicture, infrared, radar or other. The vessel identification andcharacteristic information for the vessel can include at least one of avessel type of the vessel, a name of the vessel, a number associatedwith the vessel, a status of the vessel, a size of the vessel, and acapacity of the vessel. The port data associated with the port caninclude at least one of an operational status of the port, a position ofthe port, a capacity of the port, a size of the port, a number andlocation of berths within a port, draft restrictions at the port, cargoshandled by the port, and cargos handled by the berths within the port.

The method includes receiving and combining, at the server, railinformation including at least one of more of a location of a train on arailway, a type and size of a rail car, the current and historical speedof a train on the railway, information about a shipper, informationabout a destination, and information about the location of railways, toyield second combined data (1104), and receiving and combining one ormore of information associated with factories, information associatedwith warehouses and storage locations, and a volume of rail activity, toyield third combined data (1106). Based on the first combined data, thesecond combined data and the third combined data, the method includesperforming on or more of: (1) inferring a loaded or empty status of oneor more of the vessel and the rail car; (2) inferring a cargo type forcargo on one or more of the vessel and the rail car; (3) quantifying anamount of cargo on one or more of the vessel and the rail car; (4)aggregating the amount of cargo on one or more of multiple vessels andmultiple rail cars; (5) estimating one of an origin and a destination ofone or more of the vessel and the rail car; (6) estimating an arrivaltime of one or more of the vessel and the rail car and measuring one ormore of a quantity of vessels and a quantity of rail cars (7) (1108).

FIG. 12 illustrates an example method relates to vessels and trucking. Amethod includes receiving and combining, at a server, vessel positioninformation for a vessel, vessel identification and characteristicinformation for the vessel and port data associated with a port, toyield first combined data (1202). The vessel position information forthe vessel is determined from at least one of an automaticidentification system (AIS) message (or any other message) from thevessel and an image of the vessel, which can be a picture, infrared,radar or other. The vessel identification and characteristic informationfor the vessel can include at least one of a vessel type of the vessel,a name of the vessel, a number associated with the vessel, a status ofthe vessel, a size of the vessel, and a capacity of the vessel. The portdata associated with the port can include at least one of an operationalstatus of the port, a position of the port, a capacity of the port, asize of the port, a number and location of berths within a port, draftrestrictions at the port, cargos handled by the port, and cargos handledby the berths within the port.

The method includes receiving and combining, at the server, truckinginformation including at least one of more of a location of a truck on aroad system, a type and size of a truck, the current and historicalspeed of the truck, information about a shipper, information about thedriver and driving hours, information about a destination, andinformation about the location of road systems, to yield second combineddata (1204) and receiving and combining one or more of informationassociated with factories, information associated with warehouses andstorage locations, and a volume of trucking activity, to yield thirdcombined data (1206). The volume of trucking activity can includecoverage of trucking traffic or movement. The method includes, based onthe first combined data, the second combined data and the third combineddata, performing one or more of: (1) inferring a loaded or empty statusof one or more of the vessel and the truck; (2) inferring a cargo typefor cargo on one or more of the vessel and the truck; (3) quantifying anamount of cargo on one or more of the vessel and the truck; (4)aggregating the amount of cargo on one or more of multiple vessels andmultiple trucks; (5) estimating one of an origin and a destination ofone or more of the vessel and the truck; (6) estimating an arrival timeof one or more of the vessel and the truck; and (7) measuring one ormore of a quantity of vessels and a quantity of trucks (1208).

Another aspect relates to the superset concept of managing or inferringshipping data over multiple transportation types. FIG. 13 illustratesthis aspect. A method includes receiving and combining, at a server,vessel position information for a vessel, vessel identification andcharacteristic information for the vessel and port data associated witha port, to yield first combined data (1302). The vessel positioninformation for the vessel is determined from at least one of anautomatic identification system (AIS) message (or any other message)from the vessel and an image of the vessel, which can be a picture,infrared, radar or other. The vessel identification and characteristicinformation for the vessel can include at least one of a vessel type ofthe vessel, a name of the vessel, a number associated with the vessel, astatus of the vessel, a size of the vessel, and a capacity of thevessel. The port data associated with the port can include at least oneof an operational status of the port, a position of the port, a capacityof the port, a size of the port, a number and location of berths withina port, draft restrictions at the port, cargos handled by the port, andcargos handled by the berths within the port.

The method further includes receiving and combining, at the server, railinformation including at least one of more of a location of a train on arailway, a type and size of a rail car, the current and historical speedof a train on the railway, information about a shipper, informationabout a destination, and information about the location of railways, toyield second combined data (1304), receiving and combining, at theserver, trucking information including at least one of more of alocation of a truck on a road system, a type and size of a truck, thecurrent and historical speed of the truck, information about a shipper,information about the driver and driving hours, information about adestination, and information about the location of road systems, toyield third combined data (1306), and receiving and combining one ormore of information associated with factories, information associatedwith warehouses and storage locations, a volume of trucking activity,and a volume of rail car activity, to yield fourth combined data (1308).

The method includes, based on one or more of the first combined data,the second combined data, the third combined data, and the fourthcombined data, performing one or more of: (1) inferring a loaded orempty status of one or more of the vessel, the rail car and the truck;(2) inferring a cargo type for cargo on one or more of the vessel, therail car and the truck; (3) quantifying an amount of cargo on one ormore of the vessel, the rail car and the truck; (4) aggregating theamount of cargo on one or more of multiple vessels, multiple rail cars,and multiple trucks; (5) estimating one of an origin and a destinationof one or more of the vessel, the rail car and the truck; (6) estimatingan arrival time of one or more of the vessel, the rail car and the truckand (7) measuring one or more of a quantity of vessels, a quantity ofrail cars and a quantity of trucks (1310).

In yet another aspect, a method, shown in FIG. 14, includes receivingdigital vessel location data for a global fleet of vessels, the digitalvessel location data being at least one or radar data, image data,AIS-type data, or other data and received at least in part fromsatellite data, ground based receiver or data transmitted by one or morevessel of the global fleet of vessels (1402), inferring, at a server, acargo type of cargo in a vessel and a loaded/empty status of the vesselby combining, via a processor, on or more of the digital vessel locationdata, historical vessel location data, vessel physical characteristicsdata, port physical characteristics data and known patterns of commodityflows (1404), combining, via a processor, one or more of the digitalvessel location data, the historical vessel location data, the vesselphysical characteristics data, the port physical characteristics data,the cargo type and the loaded/empty status of the vessel to yield firstcombined data (1406), receiving and combining, at the server, truckinginformation including at least one of more of a type of a truck, alocation of a truck on a road system, information about a shipper, andinformation about a destination, to yield second combined data (1408).The method further includes receiving and combining, at the server, railinformation including at least one of more of a type of a rail car, alocation of a train on a railway, information about a shipper, andinformation about a destination, to yield third combined data (1410),receiving and combining one or more of information associated withfactories, information associated with warehouses, a volume of truckingactivity, and a volume of rail car activity, to yield fourth combineddata (1412), generating, based on one or more of the first combineddata, the second combined data, the third combined data and the fourthcombined data, a quantification of one or more of maritime trade orshipping activity related to the global fleet of vessels, rail trade orrelated shipping activity and trucking trade or related shippingactivity (1414).

While the disclosure has been described with reference to illustrativeembodiments, it will be understood by those skilled in the art thatvarious other changes, omissions, and/or additions may be made andsubstantial equivalents may be substituted for elements thereof withoutdeparting from the spirit and scope of the disclosure. In addition, manymodifications may be made to adapt a particular situation or material tothe teaching of the disclosure without departing from the scope thereof.Therefore, it is intended that the disclosure not be limited to theparticular embodiment disclosed for carrying out this disclosure, butthat the disclosure will include all embodiments, falling within thescope of the appended claims. Any feature(s) of any embodiment orexample described above can be combined with any other feature(s) of anyother example or embodiment. Moreover, unless specifically stated anyuse of the terms first, second, etc., do not denote any order ofimportance, but rather the terms first, second, etc. arc used todistinguish one element from another.

We claim:
 1. A method comprising: receiving digital vessel data for aglobal fleet of vessels, the digital vessel data being one or more ofautomatic identification system (AIS) data, image data or radar dataretrieved at least in part from one or more of a satellite, a groundbased receiver, from a respective vessel of the global fleet of vessels,or other receiver; combining, via a processor, one or more of thedigital vessel data, historical vessel location data, vessel locationdata, vessel physical characteristics data, port physicalcharacteristics data associated with a port and known patterns ofmaritime trade flows, to yield first combined data; inferring, based onthe first combined data, a loaded/empty status of at least one of avessel or a type of cargo in the vessel; combining, via the processor,one or more of the digital vessel data, the vessel location data, thehistorical vessel location data, the vessel physical characteristicsdata, the port physical characteristics data, the type of cargo and theloaded/empty status of the vessel to yield second combined data;receiving data regarding one or more of supply, demand, and amount ofavailable cargo to yield third combined data; and generating, based onone or more of the first combined data, the second combined data and thethird combined data, information relating to a supply of vesselsavailable to load at a specified port and/or deliver a cargo to aspecified port, in each case within a specified period of time; andgenerating suggestions for one or more vessels regarding future routesbased on one or more of the first combined data, the second combineddata and the third combined data.
 2. The method of claim 1, furthercomprising inferring the loaded/empty status of a plurality of vessels.3. The method of claim 1, wherein the digital vessel data comprises twoor more of the automatic identification system (AIS) data, image data orradar data.
 4. The method of claim 1, wherein the first combined datacomprises a combination of two or more of the digital vessel data, thehistorical vessel location data, the vessel location data, the vesselphysical characteristics data, the port physical characteristics dataand the known patterns of maritime trade flows.
 5. The method of claim1, wherein the port physical characteristics data comprise one or moreof an operational status of the port, a position of the port, a capacityof the port, a size of the port, a number of berths within the port, alocation of the berths within the port, draft restrictions at the port,cargo handled by the port, and cargo handled at respective berths withinthe port.
 6. The method of claim 1, wherein the radar data comprises aradar image of the vessel.
 7. A system comprising: a processor; and acomputer readable storage medium storing instructions which, whenexecuted by the processor, cause the processor to perform operationscomprising: receiving digital vessel data for a global fleet of vessels,the digital vessel data being one or more of automatic identificationsystem (AIS) data, image data or radar data retrieved at least in partfrom one or more of a satellite, a ground based receiver, from arespective vessel of the global fleet of vessels, or other receiver;combining one or more of the digital vessel data, historical vessellocation data, vessel location data, vessel physical characteristicsdata, port physical characteristics data associated with a port andknown patterns of maritime trade flows, to yield first combined data;inferring, based on the first combined data, a loaded/empty status of atleast one of a vessel or a type of cargo in the vessel; combining one ormore of the digital vessel data, the vessel location data, thehistorical vessel location data, the vessel physical characteristicsdata, the port physical characteristics data, the type of cargo and theloaded/empty status of the vessel to yield second combined data;receiving data regarding one or more of supply, demand, and amount ofavailable cargo to yield third combined data; and generating, based onone or more of the first combined data, the second combined data and thethird combined data, information relating to a supply of vesselsavailable to load at a specified port and/or deliver a cargo to aspecified port, in each case within a specified period of time; andgenerating suggestions for one or more vessels regarding future routesbased on one or more of the first combined data, the second combineddata and the third combined data.
 8. The system of claim 7, wherein thecomputer readable storage medium stores additional instructions which,when executed by the processor, cause the processor to performoperations further comprising: inferring the loaded/empty status of aplurality of vessels.
 9. The system of claim 7, wherein the digitalvessel data comprises two or more of the automatic identification system(AIS) data, image data or radar data.
 10. The system of claim 7, whereinthe first combined data comprises a combination of two or more of thedigital vessel data, the historical vessel location data, the vessellocation data, the vessel physical characteristics data, the portphysical characteristics data and the known patterns of maritime tradeflows.
 11. The system of claim 7, wherein the port physicalcharacteristics data comprise one or more of an operational status ofthe port, a position of the port, a capacity of the port, a size of theport, a number of berths within the port, a location of the berthswithin the port, draft restrictions at the port, cargo handled by theport, and cargo handled at respective berths within the port.
 12. Thesystem of claim 7, wherein the radar data comprises a radar image of thevessel.
 13. A system comprising: a first data receiving componentconfigured to receive digital vessel data for a global fleet of vessels,the digital vessel data being one or more of automatic identificationsystem (AIS) data, image data or radar data and being retrieved at leastin part from one or more of a satellite, a ground based receiver, avessel, or other source; a first data combiner configured to combine,via a processor, one or more of the digital vessel data, historicalvessel location data, vessel location data, vessel physicalcharacteristics data, port physical characteristics data associated witha port and known patterns of maritime trade flows, to yield firstcombined data; an inferring component configured to infer, based on thefirst combined data, a loaded/empty status of at least one of a vesselor a type of cargo in the vessel; a second data combiner configured tocombine, via the processor, one or more of the digital vessel data, thevessel location data, the historical vessel location data, the vesselphysical characteristics data, the port physical characteristics data,the type of cargo and the loaded/empty status of the vessel to yieldsecond combined data; a second data receiving component configured toreceive data regarding one or more of supply, demand, and amount ofavailable cargo to yield third combined data; a first generatingcomponent configured to generate, based on one or more of the firstcombined data, the second combined data and the third combined data,information relating to a supply of vessels available to load at aspecified port and/or deliver a cargo to a specified port, in each casewithin a specified period of time; and a second generating componentconfigured to generate suggestions for one or more vessels regardingfuture routes based on one or more of the first combined data, thesecond combined data and the third combined data.
 14. The system ofclaim 13, wherein the inferring component is further configured to inferthe loaded/empty status of a plurality of vessels.
 15. The system ofclaim 13, wherein the digital vessel data comprises two or more of theautomatic identification system (AIS) data, image data or radar data.16. The system of claim 13, wherein the first combined data comprises acombination of two or more of the digital vessel data, the historicalvessel location data, the vessel location data, the vessel physicalcharacteristics data, the port physical characteristics data and theknown patterns of maritime trade flows.
 17. The system of claim 13,wherein the port physical characteristics data comprise one or more ofan operational status of the port, a position of the port, a capacity ofthe port, a size of the port, a number of berths within the port, alocation of the berths within the port, draft restrictions at the port,cargo handled by the port, and cargo handled at respective berths withinthe port.
 18. The system of claim 13, wherein the radar data comprises aradar image of the vessel.